{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>510</td>\n",
       "      <td>123200</td>\n",
       "      <td>29510123200</td>\n",
       "      <td>1232</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1113329</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-90.22163 38.61160, -90.22153 38.612...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>510</td>\n",
       "      <td>124600</td>\n",
       "      <td>29510124600</td>\n",
       "      <td>1246</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>2152098</td>\n",
       "      <td>578571</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>POLYGON ((-90.22765 38.58271, -90.22753 38.583...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>510</td>\n",
       "      <td>125500</td>\n",
       "      <td>29510125500</td>\n",
       "      <td>1255</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1099352</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>POLYGON ((-90.21128 38.62764, -90.21126 38.627...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29</td>\n",
       "      <td>101</td>\n",
       "      <td>980000</td>\n",
       "      <td>29101980000</td>\n",
       "      <td>9800</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>17306316</td>\n",
       "      <td>48221</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-93.59588 38.73159, -93.59583 38.732...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>101</td>\n",
       "      <td>960900</td>\n",
       "      <td>29101960900</td>\n",
       "      <td>9609</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>707732249</td>\n",
       "      <td>3270288</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-94.12917 38.65957, -94.12889 38.664...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20 NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        29        510    123200  29510123200   1232  Census Tract   G5020   \n",
       "1        29        510    124600  29510124600   1246  Census Tract   G5020   \n",
       "2        29        510    125500  29510125500   1255  Census Tract   G5020   \n",
       "3        29        101    980000  29101980000   9800  Census Tract   G5020   \n",
       "4        29        101    960900  29101960900   9609  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S    1113329         0  ...        0        0        0        0   \n",
       "1          S    2152098    578571  ...        0        0        0        0   \n",
       "2          S    1099352         0  ...      224      224        0        0   \n",
       "3          S   17306316     48221  ...        0        0        0        0   \n",
       "4          S  707732249   3270288  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0   \n",
       "1        0       10        0        0       10   \n",
       "2        0       60        0        0       60   \n",
       "3        0        0        0        0        0   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-90.22163 38.61160, -90.22153 38.612...  \n",
       "1  POLYGON ((-90.22765 38.58271, -90.22753 38.583...  \n",
       "2  POLYGON ((-90.21128 38.62764, -90.21126 38.627...  \n",
       "3  POLYGON ((-93.59588 38.73159, -93.59583 38.732...  \n",
       "4  POLYGON ((-94.12917 38.65957, -94.12889 38.664...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/mo_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "208d6842-f420-4205-859d-888e06316d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1654 popn tracts for MO\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"MO\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population MO voter-age population\n",
      "state pop= 6154913 VAP pct Hispanic is  0.04128748315399157\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP MO\n",
      "total state pop= 6154913 , VAP pct Black is  0.10850253328788018\n"
     ]
    },
    {
     "data": {
      "image/png": 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V7z3GGL9wGJY8HJ1kceQkCyKmSdSZtLGZfA942k0IuQ74Jk5Qe05ErgE2AZcCqGqJiDyHE2yCwA2qGnI/53rgCaAjMN/9Mcb47fwU5nwPNi+GEWfBEefW/R5j6kG0ja1WLSws1KKionRXw5iWUfykk2SxXUeYfC8cN80WFpoGEZFiVS2M91qds7ZEZFycsq82RcWMMc2sxzAYNRluXAbHX25BxDSLVKb//tWd/guAiFwO/KT5qmSMabDqQ7DgZ84PwLBT4etPOdN7jWkmqQSSrwFPishRInId8F3gnOatljGm3jYtdpIsvvsHOLDLkiyaFlPnYLuqrhORacCLOAsDz1HVg81dMWNMiior4I17YOlfofsg+MZsOPysdNfKtCHJFiSuIHqBXw+c/UiWiAiqemxzV84Yk4J9W5ydC0/8Npz5U2h/WLprZNqYZC2SKS1WC2NM/RzYAyWz4YRrofcoJ8mi7Vho0iRhIFHVjQAiMgEoUdUK93kX4GhgY4vU0BhTQxU+fgle/qGTsXfYaU5+LAsiJo1SGWx/EPjC93y/W2aMaUkV25wEi89fDV0HwvR/W5JFkxFSWdku6lu1qKphEUnXinhj2qZwCB6fDBVbYdI9MOEGyLH/hiYzpPIvcZ2IfJ+aVsh3cdKaGGOaW3kpdBngJFk8/3fQfSj0OjzdtTImSipdW98BTgI+x8m4eyLOroTGmOYSDsHih6KTLB5+tgURk5FSWUeyA2dfEmNMS9i5Gl66EUqXwuGT4AjbQdpktjoDiYh0AK7B2TO9g1euqt9qxnoZ0zYV/Q3m/wjyDoOLHoFjv275sUzGS6Vr6+9AP+Bc4C2cDaQqmrNSxrRZPUfAkVPghqVw3GUWRExWSGWw/XBVvVREpqrqkyIyA2f3RGNMY1UfhH//GhCY9DMnyeKwU9NdK2PqJZUWSbX7514RGQN0A4Y2W42MaSs2vAcPngzv/REq91mSRZO1UmmRPCIi+cBPcba9PQy4q1lrZUxrdmgfLLjbmY2VPxSumgPDT0t3rYxpsFRmbT3qPnwLGN681TGmDajYBh/MgIk3whl3Ql7ndNfImEZJlv331mRvVNU/NH11jGml9u92kiyOvw56HwE3f2SbTZlWI1mLpEuL1cKY1krVCSAv/wgOlcPwM5xFhRZETCuSLPvvz1qyIsa0Ovu2wr9uhdUvw4AvwdQ5tjLdtErJurY6AJcBZcBc4H+AU4HPgJ+r6q4WqaEx2Sgcgr+d5yRZPOcXcOL1lmTRtFrJ/mU/hTP1tzPwA2Al8GfgFOAJbOMrY2rbu8lJ8R7IgfN/78zK6jki3bUyplklCyRHq+oYN2V8qap68xNfEZEPW6BuxmSPcAgWPwhv/sJJ837idNs33bQZyQJJFYCqBkVkS8xroearkjFZZvvHMOdG+LzYSbB45PnprpExLSpZICkQkT8B4nuM+3xgs9fMmGyw7DGYfxt06AqXPAZjLrH8WKbNSRZI/sf3uCjmtdjnxrQtqk7A6D0KRl8Ik++Fzr3SXStj0iLZ9N8nW7IixmSFqgOw8JfOYPqke2DoKc6PMW1YKkkbjTEA69+BB0+CRX+Gqv0pJ1ks3ljGAwvXUryxrJkraEx62MR2Y+pyqBxevwuKn4D8YXD13JRTvRdvLOPKRxdTFQyTlxvg6WsnMG5IPsUby1i8bjcThvdk3JD85q2/Mc0slR0ST1bV9+oqM6bVqtgOHz0HJ30PTr8T8jql/NbF63ZTFQwTVqgOhlm8bjdA3OBiTLZKpWvr/hTL6kVEckTkfRGZ5z7vISKvi8ga989837F3iMhaEVktIuf6yseJyAr3tT+J2HQZ00T274IlDzuPex8BN69wVqjXI4gATBjek7zcADkC7XIDTBjeM2FwMSZbJUuRMhE4Cegdkwm4K5DTBN99E7DK/TyA24E3VPVeEbndfX6biBwNTMPZM34AsEBEjlDVEPAgMB1YDLwMTAbmN0HdTFulCitmOfumV1bAiLOc/FgNnJE1bkg+T187oVY3Vl5ugOpgOBJcjMlmybq28nA2scolOhPwPuBrjflSESkAzgd+CXhBaipwuvv4SeDfwG1u+TOqWgmsF5G1wHgR2QB0VdVF7mc+BVyIBRLTUOWlMO9WWPMqDCyEqX9ukiSL44bkR3VdJQougI2dmKyUbPrvW8BbIvKEqm5s4u+9D/gR0QGqr6pudb97q4h4ebYH4rQ4PKVuWbX7OLa8FhGZjtNyYfDgwU1QfdPqhILwxPnwxQ4499dw4redKb7NJDa4QOKBeWMyXSpjJI+KSHfviYjki8irDf1CEZkC7FDV4lTfEqdMk5TXLlR9RFULVbWwd+/eKX6taRPKNjp5snJyYcp9cP1/YOJ3GxREGjvN18ZOTLZKZfpvL1Xd6z1R1TJfa6EhTgYuEJGvAB2AriLyD2C7iPR3WyP9gR3u8aXAIN/7C4AtbnlBnHJj6hYKwuK/OIsLJ93jtEBGnNHgj2uK1oQ3MG9jJybbpNIiCYtIpD9IRIaQ4M4/Fap6h6oWqOpQnEH0N1X1G8Ac4Gr3sKuBl9zHc4BpItJeRIYBI4GlbjdYhYhMcGdrXeV7jzGJbVsJj50Nr//UGUw/6oJGf2RTtCa8sZNbzxll3Vomq6TSIvkx8K6IvOU+PxV3vKGJ3Qs8JyLXAJuASwFUtUREngM+BoLADe6MLYDrcfZG6YgzyG4D7Sa5pX+FV26HDt3ha3+D0Rc1SZLFpmpNxBs7MSbTiaaQ5kFEegETcMYlFmXz7oiFhYVaVGQ5J9scL8nihvdg+ZPOgHrnpu06yvQZV5leP5PZRKRYVQvjvZZqipQQzphFB+BoEUFV326qChrTbKr2O5tNBXKcBYVDT3Z+mkEmtyZsRphpTnWOkYjItcDbwKvAz9w/727eahnTBNb9G/4y0RlUD1alnGQxFdmWiNFmhJnmlEqL5CbgBGCxqp4hIkfiBBRjMtPBvfDaT+D9v0OPEfDN+TDkpCb7+Gy8u7cZYaY5pRJIDqnqIRFBRNqr6iciMqrZa2ZMQ+3fCStnw8k3w+m3Q7uOTfrx8e7uMz2QJFtNb0xjpRJISt0FiS8Cr4tIGbZew2SaL3bAyhdgwvXQa6STZLGJB9M92Xp3n8ljOCa71RlIVPUi9+HdIrIQ6Aa80qy1MiZVqk6K91ducwbWR54DPUdQvCvA4qVrm+Xu2+7ujYmWNJCISAD4SFXHQCT/ljGZYe9mmHcLrH0dCsY7SRZ7jmiRMQy7uzemRtJZW6oaBj70r2w3JiN4SRY3/gfO+1/41ivQ2xm6y5QZStk2s8uYhkpljKQ/UCIiS4H9XqGqNj6vhDH1tWc9dB/sJFm84E/O1rf5Q6IOyYQxjGyc2WVMQ6USSGyqr0m/UBAW3Q8Lf+0kWZzwHRh+etxDM2EMIxtndhnTUKkEkq+o6m3+AhH5DWDjJaZlbP0I5twIWz+EI6fA6AujXo6X+iPdYxiZ0CoypqWkEkgm4exU6HdenDJjmt6SR+DVO6BjD/j6U3D01KiXM7ULKRNaRca0lGR7tl8PfBcYLiIf+V7qArzX3BUzbZyXZLHvaHYNn8qLfW7gS52HMy7msPp2IbVk4sJ0t4qMaSnJWiQzcNKy/xq43Vdeoap7mrVWpu2q/ALe/DkEcik+8oe88H5XZn1yKcGSbeS9s6NWiyNRF1K8gJGprRdjsl2yPdvLgXLg8parjmnT1r4Bc2+G8s1sP+pqrnx0EZXVGtlFrToYZvby0qgAEa8LKVHAsAFwY5pHqmnkjWk+B8vg1R/DB09Dz5HwzfnMWtebquDqSBARICcnwPNFmwmGNSpAxHYhJQoYdQ2A234dxjSMBRKTfvt3wccvwSm3wmm3UbzlIFv2lpIbEEJhJScgXFo4CAWeWbqpzhZFooCRbADcur2MaTgLJCY9KrbDylkw8YaaJIudekRd0HNzAlw2fhCXjC2IdFnNXl5a55TaZAEj0QC4dXsZ03AWSEzLUoUPZ8Ird0D1QThiMvQcAZ16ANEX9FAozMDuHaPWhqQ6pba+M6byO+UREAFVW/dhTD1ZIDEtp2wjzLsZPnsTBk2AC+53gohPXeMYzTGltnhjGffMKyGsSiAg3DVltLVGjKkHCySmZYSC8OQUOLAHvvI7KLwGArVzhjZ0IV9jBsr9rSBBKTtQ1WzfZUxrZIHENK/dn0H+UCfJ4tQHIH8oxeVdWPzWurgD3t4F+oYzDk/5Kxo7UF6fdCY2KG9MbRZITPMIVcN7f4S3fgOTfu4kWRx2avRgujsb6+KxBQD1ukD7g05jB8rr0wrKpEF5axmZTGGBxDS9LR84SRa3rYCjL4QxF0de8l+Iq0LKjCWbeGF5KRePLUj5Ah3bKrhryuhGJ0hMdewlU5IxWsvIZBILJKZpLX4IXr0TOveCy/4BR3016mXvQlxZHUYBBaqqw5R8Xh5ZN1LXBTq2VVB2oCru6vZU79brc2ymJGPMpJaRMRZITNPwkiz2PxaOuxzO/QV0jD/+8fS1E3hheSmziksJBsOEgRWfl9daN5JIvFaBv0UR724daNBCxExLUe/VJ79TXka0jIwBCySmsSorYMHPILc9nPtLGHKS80P0Re+eeSVRF+tfXXQMl4wt4L4Fn/Le2l1x140kUlerIPZuffbyUl5YXho3WCS7s8+07qN4XXplB6psjMSknQUS03BrFjjrQspLYcJ3a1olRF/0AiKEVWtdrMcNyefms49g2YY99b6zTtYqiG2xKCQMFsnGPDKt+yhel159ZrcZ01wskJj6O7DHGQf5cCb0GsUn58/i76V90RdXRrql/Be9sDr5snKoGf+I7epqyjGH2BYLkDC1SrLWTaYMrGdqfYzxiKrWfVQrUlhYqEVFRemuRnbbtRYeOR0mXM/yodcw7fH3qQqGAcjLEWZOnwjA1x/6DyH3n1dujnBZA6f6JpPqQHlDp8pm2hTbTKuPaTtEpFhVC+O91uItEhEZBDwF9APCwCOq+kcR6QE8CwwFNgBfV9Uy9z13ANcAIeD7qvqqWz4OeALoCLwM3KRtLTK2lIpt8NFzcNL3oNfhcMsK6JjPooVrqXaDCEB1SCPjIiHf30TQfTJuSD4PLFxbMwU4GOa+BZ9y89lH1PvCWJ8xjIYOkGfaLoeZVh9jAGrnqGh+QeAHqnoUMAG4QUSOxtmF8Q1VHQm84T7HfW0aMBqYDPxFRHLcz3oQmA6MdH8mt+SJtAmqsPzv8OfxsPCXsGedU96xZoyhXW7NP6N2OcKE4T2Zv3JrrY96vmgzxRvLIl00AYGwwrtrdnHlo4sp3lhWr6rFG8MwxrS8Fg8kqrpVVZe7jyuAVcBAYCrwpHvYk8CF7uOpwDOqWqmq64G1wHgR6Q90VdVFbivkKd97TFMo2wB/v9BZXNhvDHznvVpJFscNyWfmdRO48sTBXHHiYGZOn8i4IfmcN6Z/rY8LhTUyYP30tRM4+fBeCM5akoYEAi9jb0Co15hB8cYyHli4tt6ByxgTX1oH20VkKPAlYAnQV1W3ghNsRKSPe9hAYLHvbaVuWbX7OLY83vdMx2m5MHjw4CY8g1YsFIQnvwoHyuD8P8C4b0aSLPr76cFpGVwcs/ZjVL8uTDq6L+t37Wf9ri8IhyEnIFGbTDV0xpZXh3vmlUQ2vko1Y2/xxjIuf2QR1SGlnTueE+99xRvLeGF5KQK1zs0YEy1tgUREDgNeAG5W1X3iThuNd2icMk1SXrtQ9RHgEXAG2+tf2zZk92cU7+vG4g17OXvCbxh11LHQrSDy8owlm7jrpZWEVckNCIgQDEWPUcTm0wqIEKZmarCnMavEvW4tBVTrztjreWF5KVXueE1VSHlheWmt7/WCjXfc88WlzLzOUpAYk0haAomItMMJIk+r6my3eLuI9HdbI/2BHW55KTDI9/YCYItbXhCn3DREqBrevY/wW//LK8FpPFo1md8LXLBhJyP7HiK/Ux4rt5Tz7LLNhMLOBbY6pKgbu6t86yyixi7ci7HiLDiMXYsRO3ic6qykioPVkcciQn6nvJROM/buI97dyOJ1uyP1hsxYQ2JMJkvHrC0BHgNWqeoffC/NAa4G7nX/fMlXPkNE/gAMwBlUX6qqIRGpEJEJOF1jVwH3t9BptC6fL4c534PtK1nS6XRm75no3unDix8kjs3uhoKAM2juXcz96x1y3FZLKNR0KdpnLNnEQ2+vizwPhpV75pUwql+XOvNsXTy2gOeLa9aUeNOR/SYM70m7HIm0SGzNhjHJpaNFcjLwX8AKEfnALbsTJ4A8JyLXAJuASwFUtUREngM+xpnxdYOqhtz3XU/N9N/57o+pj8UPwqt3UtWxNzeFfsj8PWNTeltuQDjjyD4s+Hg7ijNrw+teircgsL4p2quqE08LfnbZplrv9aYRnzemfyQdiz9NvT9P1szranen+dO5lB2o4u4LxrByS3mTjZHY+g/TmtmCxLbKTWfyydLXCb3/NLN7TufxorL4g0yuAM7CQv8eIqkMXKeieGMZs5eX8nzRZrfLzOl2at+udsvkuqeKeP3j7bU+Q3AG9L10LCT5jNjv9lpCzi6Jdb+nvueWSTm7jGmIjFqQaNLs0D5Y8P8gtwPFR/2IK+cGqay+lMCGsqiuKiAyNTc3IFx7yjC6dGxX6y7eGUCvPZBel9iZX96FNidQ8zlejqzY8YnvnDaChZ9sJxiGHIExA7vxUWk5CoTDzr7rqhpJU1/XGIe/JUSK76mPTMvZZUxTs0DSlnz6mpNksWIrTLyBxZ/tiuwLElIIiHNHr6rk5gQ4/Yje9OrSnjEDulF2oIr8TnmRtR7jhuRH8lclGkhPJPYO3b+plYY0qlUUEKk1PjFuSD7PfvukWoHIG/e4a8poVm4pZ1ZxKaGQE5y27D1I8cayuPWbMLwnuTmByLkEaNpxEcuRZVo7CyRtwf7d8MrtsOI56H0UfP0pKChkwsYycgJrCIZrLt2XnTAIwVmFvmDVdnIDwiwpJRiK7va5a8poni/aHLno5+SkfoGMvUMXqBmczwmAKsGQIgJnHtkn7mfEzvaKN434krEFkX1PZi51dmJM2K3kNsVyc4SvF9a9J0p9ZMpmWMY0FwskbcGhvfDpK2w5/iZe6nI540P9GIdzgbtn6pjIupC83ACXjC1g8brdBMPqm75b00rwun3mr9waCUACfG1c6hfe2Dv0i8cWcLH7vV4w8gLAglXbeXvNzjrHFeLloPKmIntBMFG3kne+CmhYGdi9IwAPLFzbpBmJLYCY1soCSWu1bwt89Bwzci/i2aKt5Hd7jP8sCxIMryP3zfVcWjiI0W6X1T1Tx9TaIMnr6snJcRYUehdjr9vnvDH9WbZhD1XVYQIBYcyAbilXbfW2Ckb17UKfrh34zmkjomZUefwBIN44SapS6VaKPSa/U17CwXGbfdUy7PecXSyQtDaqsPxJQq/8hGB1FQ9XdmGj9os6pCqkPL3EmUKbcIaS29UTAO7+6ujIGElswPFaM/51HMnMWLKJO/+5wn1Wzhmj+sR9T36nvMjgt3+NSl1iL0CpdCvFHpNocLwxs6/swpg6m+WWfSyQtCZ71sGc78OGd1gaPprbq6+tFURixZuh5O/qCYU14U58ZQeq4u58GMt/EY3NCjx/5VauOLF2/rOyA1WRWWP+NSrJJLoApdKtFHtMvFZMQ2dfZeKFMZMDm81yyz4WSFqLUJDyh84jt6qcX1Zfw8zQGWic5M7exTmqTIjMagLncW5ACIU1bneQf/Feom6jRPu1//fEobyzZlfkOH+WYP/FbcLwnrRvV7+ZTonSyie7YMa7oCZqxTR09pW/Xoeqwzz81mc8clXc6fgJNeWFvzkCW1PWz2a5ZR8LJFlu5UdFvL3rMD7dcZCtFdexUfuwjcT/8eItOAwrzFy6ieeLNhMGQiElJ0e4bPzgWrOXYi9Cd00ZXau7y3+MQGSDq+pgmC4d2/Gri45h/sqtnDemf6Q1Eu/iVt+ZTvUZ60j0nf5gEm/wviGzryYM70kgIITdX8RrH29nxpJNcVti8TT1hb+p7/ibun42yy37WCDJVsEqtsz7JUe8fz+zQ1fwYvA84KgGfZQ3FlHlS1QY9D32z156YXlpZO1JdTAct9srdoGfx5siPG5Ifq2LaLyL2w1nHF6vi0iqYx3gXPzuW/Bp1Ouzl5fWefFqyOyrcUPyGd2/Kx+WlkfKEnXpxdPUF/6mvuNvjq4om+WWXSyQZKFVRQvps/AHDNj/GS+GT+KfwZPjHidATo6zXN23G26t1wMQyejr3x53V0VlrdbHrOLSSKvG3yXm/0/vXai8gON9l3+KcGxXSFNd3FIZ6/DuoL36BcQJcs8XbSYY1jrvqhvSjXPZCYP5sHRF5Hm8jb8SaeoLf33v+Os6X+uKMpZrK8tsfvl3DFjyC3aQz4+rv8Wb4dpJFgMC0788PJLSBJx1GSWfl0dSiQSAk0f24uazjwCIjGfcPbckckH42rgCnlm6KTLtd3DPTmzcfaBmEaK7Cj5eF5e3MZS3urxdgv1KYvcx8SdObKrxgNiL4AML1/L711ZHzuvkkb0Y1KNT5FxzBG49Z1TcCQbx6u79/uqq74wlm2p16TXmPFpCqt1WmTx4b5qG5dpqDdwki0urhlEZPpNfV19OBZ1qHSY4XVWP/2dD1GZM/gu4FyhiM+suXrc7MtXXC0Cz3CSKYYgEkQA4ff7ujK2q6nDUokb/bKlLfAsN/bPC4nWFeK+nmggylYtXvC6S2DtoL5h6KV+S3VXH1n328lJns6wUxgeucLcjboh0dfWk2m1lXVFtmwWSTHeonJ2zb2PLfgie82uGfulMrizuRBVOX5V/Fpb/cVWw9uyg2C6N1dsquG/Bp4zu35UnFm2I20JAJLKu3esGOvnwXpw3pj93z1lJtZvKJOROF45dPOhdYLx90r0ZWYm6QuLtYAjUaqkADR7g9X4P3la68X43iT4raq+VnAArPy+PGjNqbVNVrdvKpMICSSZbPZ8Ds79Pj0M7mR2awv89uoinr53IXVNG8/i76zhUHaJ/9458sHlvJDeVv6fy9VW1Zwd5F3b/wsB31uyKBCH/xdBbXe7xcmJ5d/A1mX/jb3DlSTQja/by0tpTkWOe+8dp/Lm+LvEleox3AU/UWvF3n812WxL+HFx1rWD3Ao6X8n7F5+XNkugxU9gMKpMKCySZaP8u9rxwCz3WzWFTeBA/qr6Hj3QEgnLP3BJWfF4emRFVuvdQ5G1CdKtE1Vl5Hm/FeezmUCLOxdCfKTf27vtr4woi04EfWLiWYCjspm6v+Zx4iwfjdY9MGN4z0iU0272Q49a9XY4QDDlrWHp1aR83xfuOikoC4ozRxG61m2wMxiv3fj/xWhKpjAts2nMgko/Ma6XF24SrNYjttrLxEBPLAkkGWrluM4M/W8Afgl/jwdAFVLt/TQpRU0g9AXECSDt3wd8j76zzpRfRuHfrJVv3RX3GBccNoFP73FqZchPdjcYGGS9jbyBQe//02GM/33swEkS84PLC8tJICyE3IFxx4uDI5lmziksjF38ACQhvfboz0p0WCit3zy1h5ZbyyJhMvNZKvGnJAandkkg2LjBjySbuemll5LsDUtNKawsX1UxcpW/SzwJJhviopIS1Cx7jycBF9OnWkaVVf6JcawbTvS6feHPspn95OPsqgwgwaXQ/BvfsHDX4HdvdsnjdbsIxizxeKdnGxWMLohIlelvdxpu95HV5PPzWZ2zfd4hhvToz76OtkbxbQNTMK3930DNLN5GbE4haPS8QuXiHwsqA7h0jF6ivjStg5pJNkV0TR/fvGulS8n4nVcEwM5Y4iypPH9WH3JxArX3iJwzvSUCcSQLe7zReSyLRuEDxxjLuemllVNbjxrREsvHOPtvSl2Tj7zgbWSBJt3CYV576NSevv5/DCfPHqtF8+Hk/cgOdCHhXTmq6YWIH17996nAmje4XuUt8YXkpd00ZzWUnDEIh7r4a/oSIHv++IN6F4p01u3hvzS6G9zmMb508rNaMo9XbKnjN3fLW31Lyz+IKiHDP1DFcceLgqPT0oVCYaeMHM6B7x6gpytVBZyOqDzfv5c5/ruCSsU53mn9G1WUnDGb19pK4ix6rQ8rrH28nL0c466i+9OrSPvLauCG+tPlhJa9d/JZEonGBxet2R9bbgNMNGPv+eBeuRGXZeGefTYPv2fo7zkYWSNJp92dsf3o6k/cU8W54NHcEr2Wz9gWgb7eObC8/FLXplLfm4bwx/Vm5pTzSAvHfJVYFw7X2F/F4F7TP9x6sFZD8+4Lct+DTSD6sMLB2xxfc+c8VbNq9n4rKYCRAxY6zRPhmcYVVI+M0+Z3yCLgD9N73+S+sF48tYFdFJQtX74gEqFlFm5k5fWLkwl5xsJr5K7fy3xOH0qVjO/I75fHssk21uvyCIeXNT3YQVmVW0ebIPvNXnDiYUf26NHjacPt2zkJLEbj2lGF1plyBmtllub5xpsbe2Tf0Truxd+jZNPieba2nbGaBJF1CQXjqQg7bt5v/qZ7O86HT8M9Z2rr3YNThAuS1C0SCyKxiZ9fC54tLOa6gGyJCAKcF4F3E/YkLvW6lYNjZRrddjnNcTkA4fVQfert37uOG5HPz2Uew6LPdUUEM4OF31kVmZz2zNEEQ8errm8kVDCsPv/UZb6/ZGfnOu6aMrnV37l2g/V9bHdJIupS/L9rAix9sAZzW0q8uOiYSGLy1J5Hg6F/n4qbNf94NSsnWPCS70I4bkh8Zg1KFJxZtYNLofknXxwBRQX7mkk3MdluN9b2zj5cIMyDCtacMi1p8mqj+TXWHni1rRrKp9ZTtLJC0tJ2roccIyMmFix/mtQ15PD9/e63Dwup0nQTcS+PwXp05+6i+3DOvJCr1SFUwzLINTtbenIBzUfHWhIgIFQerufyRRVF5tPzdSv6Lkn8a7D1Tx/CTf67An1nFP7U4tksptu6xvBYGQDisrNxSHllXsnjd7sg5xSZaaJfj7Nk+Y8mmSBDxPLtsU6R1cfcFYyjZUs6zyzYRCoOg5OQEogbpvXUpyYJIXSvuH313fU1usurovUriZU1+vWRb1O/Dn6PMf2cPNTnNIDoYFG8si7oR8N8shFV56O11TqsyR8DdhCxeoGjMHXo2jjVkU+sp21kgaSnBSnjn987PpJ9TPOByFq/rQ36nPHIC2wnFyYWlqpGxkbU797N+93rCYY074A5Od1JFZZC7poyOzCz667vro/r1wQk4F/um8ca7uHh3+r+Zv4plG8oSfmdDhHGCQDjsdKnd/dXRBKQmz5cAxxZ0Y/TAbpExnnvmltT6nPa5gahuo6P6dSEUJjIleWivTuzZX8me/dWR95R8Xl4rN5gn3qr1h976jDdWbXfWsMSs00Gc8aaobMci9O7SnguPHwjAo++ur/U9OQGJ2njL//5cbwZcuCb1TOzNA1p7zZASvS1yvDT6Db1Dz+axhmxpPWU7CyQtYfMymHMj7PwEjp3GrOAp3P7woshgdLwgArXv7MNut5BqTZfUm59sj0rI+HzRZih07lS9qbGxLi0cFPnPFTs194PNe5n+lJOLrFeX9hzet0ukxRPLP85SX945VwXD/Hv1jqgPa5cbYOLwnpRs3cfrJduYvbyUj+JMe96050DkAlsVDEfGSYSasZ1YH5WWc+Wji+Omv58wvCe5AXFW6weEZ5dtivrdxraWwgo/fXEFw3ofVnOhV2Xbvkoeensd8z7akvD372UVOG9Mf8oOVEUFMKhpucxfuZWqYHTyy7x2zjTvR92bBG9BZK7bIvFmq8VLo9+QO3QbazB1sUDS3P5zP7z2U+g6EK6cRXFeIbc/vCgy/hBWjbog57p7pFf7Lh4e7+49NjniPXNLIskYvQuLFxxwu0E8OQFh9IBuUanhvam5zy7bxOsfR3ezBYSo1oJfQ4NIbABavrksKpj26NiOh95eBxC1CVasbfsq45b37do+4WsKVFaH+emLK1AgNyCRgfjV2yoiXYDBeCccR0jjByyIXiwKNavyu7TPjcoqMH5ofmS6srcmx+seO29Mf5Zt2OPMqgsIo/t35bITnJxd3kQLL3VMfqe8yCSMixMM6Nc3NT/YWIOpmwWS5hIOQyAABeOh8Ftw9t3QoSuLF66Ne5eaExDOOrIP3z5tBEAkc24w5HSXeK/FGwC+66ujo5IxjhnQDXDXXAzoxt1znTGQnIBw3SnDonYs9O7MFeKmmve6dI7q14VPd3wRt+518frvjx/UnT37q9j5RSXlB4OR13dVRK+E31YRPwgkkhuQqIkBHdrlkBOQhHVVagKjNxD/zLLNDTq3VAgw9fgBjOzbhQnDe3Lfgk+jXl+6oYy8HGHa+JpFmF4esFH9ukTygs0qLmXF5+Ws2rqSki3lXDy2ILLGJ7b76eKxBU0SALzxmVNH9qZXl/Zxp5MbY4GkqR3cC6/9GNp1gq/8Fgaf6Py4vCmkVdXhSI4qr0ukV5f2kW6HX110TNzMuVB74NOfhHBXRSV3zy0hGAoTECeQfGVMP3bvr6rVjXKoOsxPXnTujHNzAgQk/kC5KnyyrYIvj+zFe2t3xW2dxMpxJ6DluHf8/oDW1GJnl23YfSDy+LC8HL6oCtX5Gc0VRMD5+5330Vae/fZQxg1xNrmKbWkFw8qmPTX1js0DNrB7x5rFoiFlxpJNUZMj/K2Pymrnfb+66JhGDTYXbyzj8r/WpJTJy5Go6eTGeCyQNKVV8+BfP4D9O+HkmyKp3/38M0m8GVPeIjyvBRKbit0v2cDnbN/uheAEhQ9LyyNjB4vW7WZoz85Rn+ddP0OhMIVD8lm2sazWWAA4F8O3k3QzxerWKY/JY/oxZkA3VrqzqZojiNQlNog0ZlynMYJhZxrz6m0VPPLOulqvewtAl6zbzaWFg6KmDN8zt4S+XTuQ49uu1z+gPm5IfmR8p8qdAv180eZI66GuAJJoRtbidbsjYzZQMxXbWiQmlgWSpvDFTnj5h/Dxi9DvGLjiWRhwfMLD/f+5vemrW/YeZKa7sVKyAc268kglu0gGQ5qwPx8RliYYVG+IPfureHbZJma0fOxI6riCbnwQZ+C+JazZXsGcD7cknTpdFVJ2VFRGZRhwbgSi6yzUbF0Mzr+p00f1iUyzrg4ps5NMdfb41/DkBGqyEIDTem6XWzOF2puKbUysQLor0CpU7oN1C+HMn8J1C5MGkVjjhuRzwxmHc/HYAvJyA+TESSLo7eXhz8jrHVdxsJr/emwJFQeryXNzVjVEc3TtJJqNlk4NDSKd2+cQaOgv1/X2ml1Jg4in3F1jcszAbgmP8bpD/fzpYAB2VFRG/t0k4l/DEww7WQi848cNyWfmdRO40t2QK9kmY6ZtsxZJQ+3dDB89A1/+IfQcAbeUQPsuDf64RIun7n15VWQltbf97V1TRlOypZzlG8uiZjf169qecqo5WJ2BV/Ast7+y7nGWupQfrKr7IKDSbQGs2laR9LjqkPKD5z5g+qkjuOLEwRyoDEa9/uYn23lj1fakaz8mDO9Jjm+yQrxs0V4+NAsiJpGsDyQiMhn4I5ADPKqq9zbrF4bDUPQYLLgbNAyjL3YCiRtEGrMC2L9A7YGFa6k4WB0JFFCT4TbRoHiiKa8mM6TaQmufG+CF5aVRm4rFozgTC+785wqWrt9da+V/ZK1Ode39Vvz/RiOJLGOyRWfzQkTTsrI6kIhIDvAAMAkoBZaJyBxV/bipv+vel1fx3tJF3M0jjGMVaw47gX8O+hFnfdGDcW4vVGP/43lTLWMXwsVqxglGJgMs3VBG8aa9BHyD63X596c7E74WpmbXynj/RhMlsrSFiCZV2T5GMh5Yq6rrVLUKeAaY2tRfcu/Lq/jr22t4UH/J4bqRH1Z/m0m7buYv7zt5rLw+5URJ+1Lh/Qd/eknyIGLahlBYa82wS+b0I3onfC0gNbtWJvo36o3VxduXJd64nTF+Wd0iAQYCm33PS4ETYw8SkenAdIDBgwfHvlynV0q2ESKHm6u+y0bty05q/rP5p0Q2ZgGY9x/cGM+wXp0pLTsQnWfL5W1HLOJsbHb7V46iX9cOURmaoWYHR//mXqn+G7WkhyZVovEWDWQJEbkUOFdVr3Wf/xcwXlW/l+g9hYWFWlRUVK/vufflVVFjFX55ORI1m6Ux+0Rc+ehiqqrDWDjJDkN6dGLTngORi3y/Lu258EsDE/5bSeT4gm6RfGGrt1c4Eyvcf1dAZM1RyZbyyF4wXnm8xaqzl5eiwJgB3WrlE/OOseBg6ktEilW1MO5rWR5IJgJ3q+q57vM7AFT114ne05BAAk4weXrpRgThzCP70Ll9bsIdCBvKn678xfdL2bTnABOG9+RAVYiSLeXsrwxRFQrRo1MeU44dQEVlkOUbyyjdewBVZ7ZPOOzcpSZr3Ag4/e9uXq6WWKQnQMe8HA6ksMo8lc9q5y6dDyv06pzHF5VBDlSHnIkITooxOrRzpuwerA7RLhAgEBBCqpF0MaP7d0WBvQeqqQyGIhl7XynZxvGDukdSmqzeVsH8lVsZ3b8r63btZ/u+Q5F8V/EuyjOWbIoc722BPNpdmOnlwfI+87wx/aN2nrSLvMlUrTmQ5AKfAmcBnwPLgCtUtXbOcVdDA4kxxrRlyQJJVo+RqGpQRG4EXsWZ/vt4siBijDGm6WV1IAFQ1ZeBl9NdD2OMaauyffqvMcaYNLNAYowxplEskBhjjGkUCyTGGGMaJaun/zaEiOwENjbw7b2A1Hd3ah3snNsGO+e2oTHnPERV4+biaXOBpDFEpCjRPOrWys65bbBzbhua65yta8sYY0yjWCAxxhjTKBZI6ueRdFcgDeyc2wY757ahWc7ZxkiMMcY0irVIjDHGNIoFEmOMMY1igSRFIjJZRFaLyFoRuT3d9WkoERkkIgtFZJWIlIjITW55DxF5XUTWuH/m+95zh3veq0XkXF/5OBFZ4b72JxGRdJxTqkQkR0TeF5F57vNWfc4i0l1EZonIJ+7f98Q2cM63uP+uV4rITBHp0NrOWUQeF5EdIrLSV9Zk5ygi7UXkWbd8iYgMrbNSqmo/dfzgpKj/DBgO5AEfAkenu14NPJf+wFj3cRec/VyOBv4XuN0tvx34jfv4aPd82wPD3N9DjvvaUmAizl5T84Hz0n1+dZz7rcAMYJ77vFWfM/AkcK37OA/o3prPGWfr7fVAR/f5c8B/t7ZzBk4FxgIrfWVNdo7Ad4GH3MfTgGfrrFO6fynZ8OP+sl/1Pb8DuCPd9Wqic3sJmASsBvq7Zf2B1fHOFWfvl4nuMZ/4yi8HHk73+SQ5zwLgDeBMagJJqz1noKt7UZWY8tZ8zgOBzUAPnC0y5gHntMZzBobGBJImO0fvGPdxLs5KeElWH+vaSo33D9RT6pZlNbfJ+iVgCdBXVbcCuH/2cQ9LdO4D3cex5ZnqPuBHgH8T4tZ8zsOBncDf3O68R0WkM634nFX1c+B3wCZgK1Cuqq/Ris/ZpynPMfIeVQ0C5UDPZF9ugSQ18fpHs3retIgcBrwA3Kyq+5IdGqdMk5RnHBGZAuxQ1eJU3xKnLKvOGedOcizwoKp+CdiP0+WRSNafszsuMBWnC2cA0FlEvpHsLXHKsuqcU9CQc6z3+VsgSU0pMMj3vADYkqa6NJqItMMJIk+r6my3eLuI9Hdf7w/scMsTnXup+zi2PBOdDFwgIhuAZ4AzReQftO5zLgVKVXWJ+3wWTmBpzed8NrBeVXeqajUwGziJ1n3OnqY8x8h7RCQX6AbsSfblFkhSswwYKSLDRCQPZwBqTprr1CDuzIzHgFWq+gffS3OAq93HV+OMnXjl09yZHMOAkcBSt/lcISIT3M+8yveejKKqd6hqgaoOxfm7e1NVv0HrPudtwGYRGeUWnQV8TCs+Z5wurQki0smt61nAKlr3OXua8hz9n/U1nP8vyVtk6R40ypYf4Cs4M5w+A36c7vo04jxOwWmmfgR84P58BacP9A1gjftnD997fuye92p8s1eAQmCl+9qfqWNALhN+gNOpGWxv1ecMHA8UuX/XLwL5beCcfwZ84tb37zizlVrVOQMzccaAqnFaD9c05TkCHYDngbU4M7uG11UnS5FijDGmUaxryxhjTKNYIDHGGNMoFkiMMcY0igUSY4wxjWKBxBhjTKNYIDGmntysut9tws87XUROilM+VERKRSQQU/6BiIx3H98iIodEpFvM55W7qVFWicj/a6q6GhOPBRJj6q87TobUWkQkpwGfdzrOCuwoqroBJ+fRl32ffyTQRVWXukWX4yyYvSjm7e+okxqlEPiGiIxrQL2MSYkFEmPq715ghNsy+K3bAlgoIjOAFQAi8qKIFLt7Y0z33ijOvjbLReRDEXnDTZz5HeAW9/O+HPNdM3FW43umuWWIyAjgMOAnOAGlFlXdDxQDI5rixI2JxxYkGlNP7sV/nqqOcZ+fDvwLGKOq692yHqq6R0Q64rQYTsO5cVsOnKqq633H3A18oaq/i/Nd/YD3gUGqGhSRVcClqrpSRH6Ck2Dvl8A6YLyq7nDr80NVnSIiPXECyfmqWtJMvxLTxuWmuwLGtBJLvSDi+r6IeN1Ng3ByHPUG3vaOU9WkifDcY7aJSAlwlohsB6pV1dsZbxpwkaqGRWQ2cCnwgPval0XkfZy0+fdaEDHNyQKJMU1jv/fAbRGcjbM50AER+TdO/iKhYenIve6t7dR0ax2LE5xed3dIzcNplXiB5B1VndKA7zKm3myMxJj6q8DZpjiRbkCZG0SOBCa45YuA09wsrIhIjxQ/7wWcxJqX4aTBB2dM5G5VHer+DAAGisiQBp2RMY1ggcSYelLV3cB7IrJSRH4b55BXgFwR+Qj4ObDYfd9OYDowW0Q+BJ51j58LXJRgsB1V3et+xnZf99k04J8xh/6T6IF5Y1qEDbYbY4xpFGuRGGOMaRQLJMYYYxrFAokxxphGsUBijDGmUSyQGGOMaRQLJMYYYxrFAokxxphG+f953ErPJUcD6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for MO\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ad2HNFjUKAeW0Sl2aM3nuB45k5ue7XjoETDfL08DBrvbdEXF+RFwFbAOeaA4zvBoRNzSf+dtd24yUzLw7M7dm5iSdn59vZebtWLOeMvP7wEsRcU3TtAN4HuvWywnghoi4sPmz7qDzPbE1W8ywz9IYxAO4mc7Zat8BPjPs/gy5Fr9JZ6j/78DTzeNm4BeAWeBY87yha5vPNLU7SteZQMAU8Gzz2p/TzDwyyg/gPfzfWXzWbPl6/Row1/y8/S1wmXVbtmZ/DHy7+fP+NZ0z9KzZIg+nOpIklTQKh/gkSSPIgJIklWRASZJKMqAkSSUZUJKkkgwoSVJJBpQkqaT/Aa6XOjtCEvDIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for MO\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n"
     ]
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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niUTC1Gz7sDs8PnjzdTasiD4SNC07h4JYy6Nw5hzScycM6e3LRyMXDhNpaiLc2Eho715Cu3cT3rsX19nZ/VClljffHPSwaN8QPXhS8MtfkDhjRo/T+Hx+ppWcSNmqVwh1dhIYBfeyOhTdG0pkDPP5/OROnUbu1GmULP0sLhKhZse27g7zD98uZeOrLwGQMm78Ry2PWcVk5k084sIj3NRE84oVtKxcSfvGMjo+/LDHJ+3FTZxI+oUXknL66aSec3YPSxoYiz0VL37KlENON+34k3j3xWfZsf6d7jPmRiuFhcgRxHw+coqmklM0lQXnLcM5x97yHZRvXM/OsvXsWP8Om1ZFnzkRTM+IdpjHWh/jCyaN6fCof+wxdn//v3CtrfjHjyepuJiU008nkJ2FLzWNwLhMArHbnPvT0rwXOAD7br3e9NxzpF9wQa/TTSo+lvikIJvf+NuoDwvdG0rkE8Q5R92uimh4xA5dNddGb72elJrWfdiqYGYx2ZOKxswzN1w4zHslx5Fw9HRy/+3foo9nHcHaIy0tbP/SNbRv2MD4L13N+H/6p4P6LPb5y89+zPZ33+Yrv3oA3wCezzGq+ixEZGwzM8blFzAuv4BjF5+Lc46Gqt3sLFsXO113PZvfiD4eNTE5hYkzZ3cfusoumoLPNzpvetj6ZimurY3MSy4lOH/+SJeDLzmZSffeS9V//Rd7776H+kf+TNZXv0rmpZccdL3HtONOYtOqV6h4byOFs+aMUMXe1LIQkY9prKnubnWUb1xPfdUuIHpB4cRjZnWHR86Uo/CPgmdgu3CYD85bAs4x9bFH8SWPrtumtG3YQPWtP6L1jTeILypi3D9eQ9q553a3NDrb2/jltVcwd/F5nHH1dYe9nlF16uxwUViIjB5NtXuirY5Yv0ddZTkQvaAwf8bM7lN1J0ybPiJPgAs3N/P+cccTXLiQwl/dOerCAqKH/5pXrKDmttvo2LwFS0gg5cwzSL9gGSmLTuWx235IzY5tfPmOew+730hhISKjSkt9XewiwfWUb1zH3vLosxoC8QnkHz2DgpnRs63yps0YttNB995zL9U/+Qlxkwop+t3vCGSNzgsUnXO0r1tHw+NP0Pj004Tr6vBnZlJz4kJer/yQK/+/28mdeni3q1dYiMio1trYEL0te6zfo2bHNnAOf1wcedNmdHeY5x99DHEJiUNWR+V3vkPDw48w8b9vI+2884ZsPYPFdXXR/NpKqn/8Y5p2bOfF2VM44aJLOfWyqw5reQoLERlT2pubKd+0gfJYv0f1h1txLoLPH2DCUdNjFwkWk3/MLOITkwa8PtfZSfVt/03tb39LUslCJt97LzbKL3DbX6Szk4obv85ft2/CZWdxzW+XH9ZyFBYiMqZ1tLZQ8d7G7n6P3Vs3dz/TI3fqtO4O84nHzOr3Mz06tn5I+deup3PLB2ReeSW5N31rTAXFPi4S4f5LlhH2+7hm+ROHtQyFhYgcUTrb26h8r6y732P3lveJhEOH9UyPzWeeSahyF3EFBaSceQbm82MBP8T+9SUnE8jJJWlOMXGTJw/ZRYfOOcJ1dXRu20aoqgoXieA6u2hdvZr2DRtwkQjEXs65j713QFW8jzWp8UzNyWfZL3u6gbc3XWchIkeU+MQkiuYuoGjuAiD6TI9dm9+LdpiXrfv4Mz0KJ1MQuz1Jwczig57pkTjjGNpaWok0NdHw6GO4cBjC4ei/B9zmI37KFDKvuIL0z16IPyVlULep+pZbqb3vvoM+96WnE1ywAEtIwHwG5gOfr/t9XWc7b9fuoqqtmdS4BEquumZQ6xpMalmIyKgS6uxk95b3uzvMK9/fRKizA4g902PfzRFnzTnkMz2cc0RaWumqKKft7bepf/RR2t95F19yMukXXcT4a68lLjen3/U556i7/35CNTXs+/1seuZZuiorKfzVnQQm5EVbN+YjvrCgx4cuNdfuZeXyB9jwyoskJqdw0uevYO7Z5w3ouhUdhhKRT7RwqIvdH2zp7jCveK+MrvY2ADLzJnZ3mBfMmuP5TI+2d9+l9sEHaXzmWczvZ/w115D1/3z1oFuDdFVVs+eOO0iaNxdLSIy2BHx+8PuINDax6z/+AwIBbN+PuxmZV1xO7re+dcj1d7W38+aTf+bNJx/BhcPMO/cCTvzspSQOQktHYSEisp9IOEzVh1u6b09SXrah38/06Ny5k+qf/JSm556j4I6fk7r440+ErvnlL9nzs58fso6CX9xB6lln9a3mSJiNr7zEquUP0FxXy9EnnsqiK64mI3dCn+bvC4WFiMgh7Humx74O84qy9bS3NAOQmpXd3eoomFVMRm5edyd3165dbDnjTLJvvIGsr3zlY8t0XV00PP44u77zXQAyLr6YzCsuBzNcOIwF4kg4enqfOsx3rH+HFQ/cQ822reRNm8GnrvpHJh4za5D3gsJCRKRfXCTCnp3buzvMy8s20NbYAEBK5rhocMwsJvjmW3Td/yBTH3+cxBlH97isji1b2P2DH9D699dJnDWLjMsuJW3Jkj51kO+t2Mmrv/sNW9e8QVp2Dosu/yIzTj5tyM7IUliIiAyAc47aip3dtycpL1tPS30dAPFdIbKcj8Jj51Fw/ElkzTmW4IQ8fAdcq9Hw5JPsufNXdH7wAZaYSNqnzyH1058mae5cAuPHf2za1sYG/v7w73nnhWeIS0jghM9eyoLzlg35rU8UFiIigyj6TI9KysvW8eErL1OxaSNt9tHvoD8cIakrRNAZQSBoflKSkknLziE5Lp64hiba33mHSHP0UFdcYSHBE45n3Ne+xrrXX2P1o3+ks72NY886l5MvvoJgesawbJfCQkRkCDnn2LthHbveXE399u00NdXT1NxES1srLV2ddEbCH5veF3EkhsMkdXQR3xViV2b0wsGg+Wh1EaYuOI7T/uFLjC+YNKzboYvyRESGkJmRVXwsWcXH9ji+s62VxppqGvfUULtpI3vXvUtjdRXNLU00hj8KkiR/HEu+/V0mHztvmCofXgoLEZFDiE8KkjWpiKxJRaP+OdlDaWw8YFdEREaUwkJERDz1OSzMzG9mb5vZU7Hh5Wa2NvbaZmZre5nvBjNbb2YbzOzGwSlbRESGU3/6LG4AyoA0AOfcpftGmNlPgYYDZzCzYuDLwPFAJ/Csmf3FObd5IEWLiMjw6lPLwswKgPOBg260btHLES8BHuph1pnA6865VudcCHgF+OzhlysiIiOhr4ehbgduAiI9jFsEVPXSWlgPnGZm480sCCwBCntagZldZ2alZlZaU1PTx7JERGQ4eIaFmS0Fqp1za3qZ5HJ6blXgnCsDbgVeAJ4F3gFCvUx7l3OuxDlXkp2d3ZfaRURkmPSlZXEKsMzMtgF/AM40swcBzCwAXAT0+oRx59w9zrkFzrnTgFpA/RUiImOMZ1g45252zhU454qAy4CXnHNXxkYvBjY558p7m9/McmL/TiIaLD22QkREZPQa6HUWl3HAj7+Z5ZvZ0/t99IiZbQSeBL7qnKsb4DpFRGSY9et2H865FcCK/Yav7mGaSqId2fuGFx12dSIiMiroCm4REfGksBAREU8KCxER8aSwEBERTwoLERHxpLAQERFPCgsREfGksBAREU8KCxER8aSwEBERTwoLERHxpLAQERFPCgsREfGksBAREU8KCxER8aSwEBERTwoLERHxpLAQERFPCgsREfGksBAREU8KCxER8aSwEBERTwoLERHxpLAQERFPCgsREfHU57AwM7+ZvW1mT8WGl5vZ2thrm5mt7WW+r5vZBjNbb2YPmVniINUuIiLDpD8tixuAsn0DzrlLnXPznHPzgEeAPx84g5lNBL4GlDjnigE/cNmAKhYRkWHXp7AwswLgfODuHsYZcAnwUC+zB4AkMwsAQaDy8EoVEZGR0teWxe3ATUCkh3GLgCrn3OYDRzjnKoCfADuAXUCDc+75nlZgZteZWamZldbU1PSxLBERGQ6eYWFmS4Fq59yaXia5nF5aFWaWCXwGmALkA8lmdmVP0zrn7nLOlTjnSrKzs/tUvIiIDI++tCxOAZaZ2TbgD8CZZvYgQOzQ0kXA8l7mXQx86Jyrcc51Ee3XOHnAVYuIyLDyDAvn3M3OuQLnXBHRzumXnHP7WgeLgU3OufJeZt8BnGhmwVjfxlns10kuIiJjw0Cvs7iMAw5BmVm+mT0N4JxbDTwMvAWsi63vrgGuU0REhpk550a6hoOUlJS40tLSkS5DRGTMMLM1zrmSoVq+ruAWERFPCgsREfGksBAREU8KCxER8aSwEBERTwoLERHxpLAQERFPCgsREfGksBAREU8KCxER8aSwEBERTwoLERHxpLAQERFPCgsREfGksBAREU8KCxER8aSwEBERTwoLERHxpLAQERFPCgsREfGksBAREU8KCxER8aSwEBERTwoLERHx1OewMDO/mb1tZk/Fhpeb2drYa5uZre1hnhn7TbPWzBrN7MbBK19ERIZDoB/T3gCUAWkAzrlL940ws58CDQfO4Jx7D5gXm8YPVACPHn65IiIyEvrUsjCzAuB84O4exhlwCfCQx2LOAj5wzm3vb5EiIjKy+noY6nbgJiDSw7hFQJVzbrPHMi7DO1BERGQU8gwLM1sKVDvn1vQyyeV4hICZxQPLgD8dYprrzKzUzEpramq8yhIRkWHUl5bFKcAyM9sG/AE408weBDCzAHARsNxjGecBbznnqnqbwDl3l3OuxDlXkp2d3afiRURkeHiGhXPuZudcgXOuiOihpJecc1fGRi8GNjnnyj0W49n6EBGR0Wug11kc1A9hZvlm9vR+w0HgbODPA1yXiIiMkP6cOotzbgWwYr/hq3uYphJYst9wKzD+cAsUEZGRpyu4RUTEk8JCREQ8KSxERMSTwkJERDwpLERExJPCQkREPCksRETEk8JCREQ8KSxERMSTwkJERDwpLERExJPCQkREPCksRETEk8JCREQ8KSxERMSTwkJERDwpLERExJPCQkREPCksRETEk8JCREQ8KSxERMSTwkJERDwpLERExJPCQkREPCksRETEU5/Dwsz8Zva2mT0VG15uZmtjr21mtraX+TLM7GEz22RmZWZ20iDVLiIiwyTQj2lvAMqANADn3KX7RpjZT4GGXub7H+BZ59znzSweCB5mrSIiMkL61LIwswLgfODuHsYZcAnwUA/j0oDTgHsAnHOdzrn6AdQrIiIjoK+HoW4HbgIiPYxbBFQ55zb3MG4qUAP8JnYI624zS+5pBWZ2nZmVmllpTU1NH8sSEZHh4BkWZrYUqHbOrellksvpoVUREwAWAP/rnJsPtAD/1tOEzrm7nHMlzrmS7Oxs78pFRGTY9KVlcQqwzMy2AX8AzjSzBwHMLABcBCzvZd5yoNw5tzo2/DDR8BARkTHEMyycczc75wqcc0XAZcBLzrkrY6MXA5ucc+W9zLsb2GlmM2IfnQVsHHjZIiIynAZ6ncVlHHAIyszyzezp/T66Hvidmb0LzAN+OMB1iojIMDPn3EjXcJCSkhJXWlo60mWIiIwZZrbGOVcyVMvXFdwiIuJJYSEiIp4UFiIi4klhISIinhQWIiLiSWEhIiKeFBYiIuJJYSEiIp4UFiIi4klhISIinvrzpDzZT1tXB09sWs32xnKaOmvZ07aHyqYaajv20hqqp4tmcD58xOEjHr/FE/RnkJmQQ3ZSDoVpecwYP5ljJ0xh+vg8An7/SG+SiEivFBb9sLtlN3+r/Bu/eP0pqrrWYf727nEuEsCFU4knjaA/m+y4aTgidEU66HKddEXaaQxVUhvZyAcd7bxeD+zYN28ciS6f7IQipqZP44riT3NK0cwR2UYRkZ4oLA6hK9zF29Vvs7JiJSsrV7K5LvowwEhXGqGWYq6edy5zc48hPX4c45PSmZKdTELAu4Wwu6mO9VU72LRnB1vrytnWsI1dbR9S3rGG8r2v8OrLv+Nnn7qbM6bOHepNFBHpE9119gCVzZXRcKhYyepdq2kNtRLwBViQs4BTJ57K0Wkl/MMvt5OfnsSdVy2kOD8dn88Gbf0vbF7PN179J8wCrL36ZXw+dSuJiLehvuvsJ75l0RHuYE3VGlZWrGRVxSq2NmwFID85n6VTl3LKxFM4Ie8EkuM+enT4zy8fz7/+6R2W3bGKSeOCfP8zszl9Rs6A6mhq7+KlTdW8trUJ8BFPmoJCREaNT2RY7GzcyWsVr7GqchVv7n6TtlAbcb44SnJL+Nz0z3HqxFOZkj4Fs55bDBfMzeeUaVm8vKmaX67YwtW/eZPz5+Txfy6YRU5aYr/reeX9Gr547xtgIVIm34svsZnvnnDrQDdTRGTQfCLCoi3URunu0u7DSzuaoj3LhamFXDjtQk6deColuSUE44J9Xua45Hg+t7CApXPzuHPFVu54eTNVje08dN2JxPn71yKoamgHHNlTHqM9YSu3LLqVJVNP7tcyRESG0hEbFjsr3mBFxUpW1a6ndM87dIQ7SfQncNyE47li5hUsmriISfHpUL0JGqtg93LwBSAuCYLjILMI0gvBH3fI9SQE/NyweDp56Ync9Mi7fP5//8ap07OYW5DB9NxUJmYkER84dHicOHUcWYV/pT2hlK/N/xpLpi4ZxD0hIjJwR1RYRMIhVrxxOw+9/0depw2AKZ1dXNzWxqK2dha0d5C4qwG2vAvtt0D9jkMv0PwwoRhmLIGFV0PqhF4nveS4QkIRx+9Wb+fOV7YSjkRPHPAZ5KUnUTguicLMIIXjgoxLjqemqYPK+ja21DSwsfM+4jPfYFbKOVw759rB2h0iIoPmyDkbqq2eb/xmIS8kB8kNOy4ZP58lE0+jIAKE2qMthq42qNoIbXWQkAJZM2DCHEifCEnjwIWhqx1aqqFuG9RuhW2rYOdq8MdHgyOQBIGE6PLikiAhNTpv6gQYfxTkFtOeMJ4NlY1s29PC9tpWduxtYWddGztrW6lu6gDADLLTwHJ+R2tgHZdO/yL/cdK/9tpPIiJyKDobqq/igqxMSuSU1jbuuHYdgbikw19W9tFQdOpHw7Vb4fX/hb0fRIOnrQ6adkXDp6MxOuwi0WnNR+L0c1i46JssXHjcQYtu7wpT19oJvma+/sr1lNWW8Z3jv8Olx1x6+PWKiAyxIycsAvEEzMeUYN7AgqIn46bCkh/3Pj4SgeYq2LsFtr4Mbz0A934appwWnbfoFJi2GBLTSYzz00EVX3n+K+xt28vtp9/OGZPOGNx6RUQG2ZETFoDfF0eooQKqyyBnGG+X4fNBWl70NWURnHIjvPg9qFgD6x6G0nsgJRe+9Axrw01c/9L1+MzHvZ++lznZc4avThGRw3REhYUvPoWwvxme/hZc/dTIFZKYBuf/NPo+Eobtq+CPX+DFR6/i24md5AZzuXPxnRSmFY5cjSIi/XBEXSIc8MURzjoatr0GGx4d6XKifH5c9iwezD+KrwcamZExnQeWPKCgEJExpc8tCzPzA6VAhXNuqZktB2bERmcA9c65eT3Mtw1oAsJAaCh76/0uTDjUEh14/X9h9meHalXeOpphw6OsX/tbbunayTuJCZyRkMOt595LUmCQ+1RERIZYfw5D3QCUAWkAzrnu03fM7KdAwyHmPcM5t+ewKuwHf1cb4YYaSMoc3j6L/VWuhVdupWbbq/xPajyPp6YwPi6d/5pxOctKbsDn03MrRGTs6VNYmFkBcD7wA+AbB4wz4BLgzEGvrp/8GGFfAL79/sgUULWBzt9dzANBP3flZ9NlxjWzvsCXj/0yKfEpI1OTiMgg6GvL4nbgJiC1h3GLgCrn3OZe5nXA82bmgF855+7qaSIzuw64DmDSpEl9LOvj/BihkbiorbMF9/qdvPzmz/jJ+FR2+o0zCk/jmyXfZFLa4W2LiMho4hkWZrYUqHbOrTGz03uY5HLgoUMs4hTnXKWZ5QAvmNkm59yrB04UC5G7IHoFd1+KP5DffET2XRw3HGreg9V3sqXsz9yaGsfrWWkclTqZX53475ycrxsBisiRoy8ti1OAZWa2BEgE0szsQefclWYWAC4CFvY2s3OuMvZvtZk9ChwPHBQWg8EPhG0YTvBqq4cXv0fDW7/lF+PG8cecNJIDydy84GtcMuMSAr4j6oxkERHvsHDO3QzcDBBrWXzTOXdlbPRiYJNzrrynec0sGfA555pi788Bvj8IdffIb35CDFHLoroMNj4BW/5KqGINf0oJ8ouiKTS5MJfMuISvzvsqGYkZQ7NuEZERNtA/gS/jgENQZpYP3O2cWwLkAo/Gbo4XAH7vnHt2gOvsld+M8FAseNsq+G30tuGvFxzLrdNmsiXUyAm5C7jp+Js4OvPooViriMio0a+wcM6tAFbsN3x1D9NUAkti77cCcwdSYH8EMCIMQQf3+ofZGQjwk5ILeanqDSYmTuT2477PmYVn6i6xIvKJcEQdXPcN9tlQoU5anvkmv976GPcXTiSwdx03LLiBq2ZdRYI/YfDWIyIyyh1RYeEHOgapgzvSVseTf/wct4d2sycjnWVTl3LDwhvJCeYMyvJFRMaSIyosAhitNoCHOTkHVRt4Z90D3LL9SdbH+Tk2rZD/Of3HHJt97OAVKiIyxhxRYeFzrv+nzpavgU1PQcUaqvZs5PbECE+lJJMdF88PZ1/L+Qv/Bd9wnI4rIjKKHVFh4e9vWDRXw91n0m7G/QVHc3dWMmEzvjz981xb8g2CccGhK1ZEZAw5osIi4Fz01NlwF5gv9jLYsRpqP6Az1EGTGU3hNprDHTSG26lMTebu8dlUWBuLJy3mGyXfoDBVtw8XEdnfERUWcWZsCRjH3z+PJOeo9Ufv8Do+FKbZZ3T4emh1ZI1neiCFu8+8jRPyThjmikVExoYjKiy+fOLNFK2/n9ZIiLZIF39sjt7b8PT0aaTlziU1IY0UXwKpccmkBoKkBJJIi09j6qTT8PuPqF0hIjKojqhfyOlHfZrpR326e/i7I1iLiMiRRKf5iIiIJ4WFiIh4UliIiIgnhYWIiHhSWIiIiCeFhYiIeFJYiIiIJ4WFiIh4MucGcEvvIWJmNcD2ka5jALKAPSNdxCilfdM77ZtD0/7pXRaQ7JzLHqoVjMqwGOvMrNQ5VzLSdYxG2je90745NO2f3g3HvtFhKBER8aSwEBERTwqLoXHXSBcwimnf9E775tC0f3o35PtGfRYiIuJJLQsREfGksBAREU8Kiz4ys4vNbIOZRcysZL/PzzazNWa2LvbvmbHPg2b2FzPbFJvvll6WW2RmbWa2Nva6c7i2aTAN1f7ZbzmTzKzZzL451Nsy2Ibwu3P8ft+bd8zss8O1TYNlCPdNj/OPNUO4f8ab2cux/1N39KkY55xefXgBM4EZwAqgZL/P5wP5sffFQEXsfRA4I/Y+HngNOK+H5RYB60d6+0br/tlvOY8AfwK+OdLbOlr2TWy6QOx9HlC9b3isvIZw3/Q4/1h7DeH+SQZOBb4C3NGXWo6ox6oOJedcGYCZHfj52/sNbgASzSzBOdcKvBybptPM3gIKhqncYTeU+8fMLgS2Ai2DX/nQG6p9E5tun0RgzJ2tMoT7prf5OwZ5E4bUEO6fFmClmU3ray06DDW4Pge8feAX0swygAuAF3uZb4qZvW1mr5jZoiGucST1e/+YWTLwbeB7w1HgCDqs746ZnWBmG4B1wFecc6GhLnQEHO7/q0POfwQZ6P7pE7Us9mNmfwUm9DDqP5xzj3vMOxu4FTjngM8DwEPAz5xzW3uYdRcwyTm318wWAo+Z2WznXONhbcQQGqH98z3gv51zzQf+dTWajNC+wTm3GphtZjOB+8zsGedc++Fsw1AZqX1zqPlHk5HcP/2hsNiPc27x4cxnZgXAo8AXnHMfHDD6LmCzc+72XtbZAXTE3q8xsw+Ao4HSw6llKI3E/gFOAD5vZj8CMoCImbU75/rWKTdMRmjf7L/+MjNrIXr8elR9d0Zq33jMP2qM9HenrxQWAxRr6v0FuNk5t+qAcf8vkA5ce4j5s4Fa51zYzKYC04kenz8iDHT/OOcW7Tf9/wWaR1tQHK5B+O5MAXY650JmNploR+i2ISt4GA3Cvul1/iPBQPfPYRnOnv2x/AI+C5QTbQVUAc/FPv8O0Y7Xtfu9coh2KjmgbL/Pr43Nswz4fuz954h2UL0DvAVcMNLbOpr2zwHr+L+MzbOhhuq7c1Xsu7M29t25cKS3dRTtmx7nH+ntHS37Jza8DagFmmPrmHWoWnS7DxER8aSzoURExJPCQkREPCksRETEk8JCREQ8KSxERMSTwkJERDwpLERExNP/D0rA7CGlVJkyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - select ones for WA)\n",
    "tractFixList = [1056,1399]\n",
    "for tFL in range(len(tractFixList)):\n",
    "    m = tractFixList[tFL]\n",
    "\n",
    "#for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11, 22, 265, 346, 432, 736, 976, 1012, 1016, 1132, 1180, 1246, 1322]\n"
     ]
    },
    {
     "data": {
      "image/png": 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oEqITsj/GoKjYqCKeAz155PFHcG3hSgWPCkXqQaTbFS9bnCqPViHiQASmVBOOpR1x8XS5Z4JPjEtk97TdGO2NDPh9QJZtN5uwrkZepWR0Sb4c/yVjfx7LsWPHOHr0KF27duWjjz7K72IJIfJRkWiT11pjSjVx2vc0gYsDiQyIpHi54pSvV57SNUpj52D+rDIUM+BY2hFnV2eqPlYVh1L5OwG2tVzwv8D2j7dzfO1xHEo70PKtlrQc2zLbUSsXdV3EyY0naf+f9rSb1O6O7Sc2nGBx98V49vOk/9L+9/zAEEJYR17a5ItEkhfZizgYwY6PdxC8OhjHMo60GNsCr5e9MoZTiDgQwezmsylZuSRvR7x9x/GXjl5iTqs5lKtdjhd3vVikmq6EeJhIkn/IRRyMwG+KH8fXHsdQzIBnf08eG/0YS/suJT4qnn5L+9FgQIMs3SETriTw8+M/k3QtiVH+o7IdLlkIUThIkheAeSiFfd/t4/C8wyTFJWXZVqF+BZq+0JQGgxpQomIJlvRcwtntZxnuN5xqratZKWIhRE5IkhdZJN9IZs+MPWz/aDuNhjSiYoOKt54AzqT7j91pPspmRokWwmZJkhc5EnMyhuA/gkmISaB6m+rU6VrH2iEJIXLgoXgYSuRdudrlaP12a2uHIYQoQEW307gQQoj7kiQvhBA2TJK8EELYMEnyQghhwyTJCyGEDZMkL4QQNkySvBBC2DBJ8kIIYcMkyQshhA2TJC+EEDZMkrwQQtgwSfJCCGHDJMkLIYQNkyQvhBA2TJK8EELYMEnyQghhwyTJCyGEDZMkL4QQNizHSV4pZVRKHVRKrcu07g2lVIhS6qhSanr+hCiEEOJB5WaO1zFAEOAMoJRqD/QCGmutk5RSFfMhPiGEEHmQo5q8UsoV6Ab8nGn1q8A0rXUSgNb6kuXDE0IIkRc5ba75BhgPmDKtqws8oZTaq5TarpR6LLsDlVKjlFL+Sin/qKiovEUrhBAiV+6b5JVS3YFLWuv9t22yA8oCLYF3gWVKKXX78Vrr2VprL621l4uLiyViFkIIkUM5aZNvA/RUSnUFHAFnpdRCIAxYqbXWwD6llAmoAEh1XQghCon71uS11hO01q5aazfgGeAvrfUQYDXQAUApVRewBy7nX6hCCCFyKze9a273C/CLUioQSAaeT6/VCyGEKCRyleS11n6AX/pyMjDE8iEJIYSwFHniVQghbJgkeSGEsGGS5IUQwoZJkhdCCBsmSV4IIWyYJHkhhLBhkuSFEMKGSZIXQggbJkleCCFsmCR5IYSwYZLkhRDChkmSF0IIGyZJXgghbJgkeSGEsGGS5IUQwoZJkhdCCBsmSV4I8VD69/t/mdtuLrun7ybmZIy1w8k3eZn+TwghiqwzW89wfvd5zu08h+97vlRsVJH6ferj0deDSo0roZSydogWIUleCPHQiY+OJ/p4NC4NXHh27bMErw4meGUwO/6zgx0f76BsrbLU71sfjz4euLZ0RRmKbsJXBTn3tpeXl/b39y+w6wkhxO1Cd4aycvBKrkdep+ecnjQZ2iRj2/XI64SsCSF4VTCnfU9jSjFRskpJ6veuT/0+9XHzccNYzFjgMSul9mutvR7oWEnyQoiHgSnNxK7Pd+E3xY+ytcrSb0k/qjavetf9E68mcmL9CYJXBXNiwwlS4lNwLOtIvR71qN+3Pu6d3SlWvFiBxC5JXggh7uFaxDVWDVnFmb/O0GhwI7p93w0HZ4ccH5+SkMKpzacIXhlMyNoQEq8kUsypGLWfro1HXw/qdKuDY2nHfItfkrwQQtzFpaOXWPTUIuKj4+n6bVeavtA0TzdV01LSCN0eStDKIIJXBXP94nUMxQzU6liL+n3rU79XfUpULGHBEkiSF0KIbJ3fc57F3Rdj52DHc38+R+UmlS16fm3ShO0NMyf8lcFcOX0FFFRvWx2Pvh7U71OfMjXK5Pk6kuSFEOI2IWtD+H3g7zhXc2bIpiGUrVk2X6+ntSYyIJLgVcEErQzi0pFLAJRxK0PDZxviM9UHo/2D3bSVJC+EEJkcnHuQtSPXUqVZFQZvGEwJF8s2n+TEWb+z/Nr+14z3o4+NxsXD5YHOlZckL/3khRA2Q2vNrmm7+GviX7h3dmfgioHYl7Qv0BguB19m9/TdBCwMQBkVjZ5tROvxrR84weeVJHkhhE3QJs2fb/3Jvpn7aDS4Eb3m9nrg5pEHEf5vOLun7SZoVRB2DnY0f7k5rd9uTRm3MgUWQ3YkyQshiry05DRWP7+awCWBtBjbgi5fdbnnU6pdunThwIEDjBkzhkmTJnHq1CkGDhxISEgIf/75J23btgXgv//9L6tXryYtLQ13d3fmzJlDamoqPXv2JCEhgdTUVEb1HIXdVjvO/HUGxzKOPDHxCVq82cLiPWwelAxQJoQo8ta8tIbAJYF0+qITXf577wQPMGfOHGbMmJHxvkqVKmzZsoX+/ftn2e/1119nx44d7N69G4DNmzdjZ2fH7NmzmTdpHgOTBjL+g/FEBUXx5IwnGRs6lg6fdCg0CR6kJi+EsBCtdbb9z02pJgAMdvlXpzy95TSNnmtEm/FtcrS/q6trlvdOTk44OTndsZ+9vbk9X2uNyWSidu3ahO8OZ9ukbZzffR7Hao6UqlyKMWfGYOdQONOp1OSFEHmSkpDCJ60/wc3OjQZVGuBVzwu/BX7Ehsbyy0+/UN+5Pu7F3HnB9QXiL8czdepUPDw88PHxwcfHh7S0tDxdPy05jeuR1ylXp5xlCnSbTz/9lLp16xJ9OZrDnx3m1/a/Ens2lm7fdyOkYwiTP5tcaBM85KImr5QyAv5AuNa6u1JqKjASiErfZaLWeoPlQxRCFGY7PtlB7N+xjKk3hoRzCRy7eIwxw8bQlrbsVrsZrAfjVM6JlJiUjNr8Bx98wJAhQyxy/bjwONBQulppi5zvdh988AFjXx5LzyY9+XXTr4ybOA7vD735fMbnlCtfjhdeeCFfrmspufn4GQMEAc6Z1n2ttf7SsiEJIYqSs3+dxaOlByP+HoHWmj8W/0HywmRS7VJxPe/K8tDlmGJMTBg+Accy5vFdpk+fzg8//MDAgQN58803H/ja2mTuMglQvl55i5Qns8TERK6HXue37r+RejGVlqNa0vHTjnz77becOHGCX3/99f4nsbIcNdcopVyBbsDP+RuOEKKosStuhzKa2+JDtobw5vNv0iymGQnFE7B7xI5hhmG0r96eddfXAfDGG29w+PBhtmzZwpo1a9ixY8cDXdeUZmLNiDUcmH2ANu+3oVrrajk+duTIkcyYMYN58+bRu3dv4uLi6NSpE5s3b+att95iypQp5v0GjeTxBo/zdejXVH66Mu9/+z6XLl1izJgxnD59mvbt21ukySk/5bQm/w0wHih12/rXlVLDMDfjvK21vnL7gUqpUcAogOrVqz94pEIIq4gLj2P/7P2UqVkGhSL+cjw3om6QlpyGNmnObjuLa0tXjvx+hAGDBtDa1Bp9RHNm3xnKUhZTKRMfrv+QToM6AVC+vLnGXbx4cfr27cv+/ftp165drmIypZpYNWwVgb8F4j3VG+/J3rkadOynn366Y52vr2+W9wd+PkDdDXVpVa8Vz657NmNYhIoVKxbqpH67+yZ5pVR34JLWer9SyifTpu+B/wA6/edXwIu3H6+1ng3MBvOwBnkPWQiR30xpJv755h+Cfg8i7J+wO7YbHYzmm40KHJwdCD8QzrMDnqVFzRZ8v+97AFKGpLBhywb6zO9D6PVQ3N3dAYiNjaVMmTJorfHz82P48OH3jEVrTeyZWFKTUkGbm2j8pvoRtCKIjp93pO37bS1edt/3fPn7q7+p/VRt+i3pl6/DCOe3nNTk2wA9lVJdAUfAWSm1UGudcddEKfUTsC6fYhRCFKC05DTmd5zPuV3neKTFI1R9rCoX/r1A/d716fxVZ5wqOGFfyh6lFFpr/vrgL378/EdOGk9S3rU8Xft3pVGjRszcOJPYsbG88s0rmEwmZs+eDcDYsWMJCQlBa42Pjw9du3a9ayyJsYmsfG4lJzacuGNbl6+70HJsS4uWPfl6MiufW0nImhAee/0xnvr6qXzt+lkQ7pvktdYTgAkA6TX5d7TWQ5RSVbTWEem79QEC8ytIIUTBuR55nXO7zgHwT+1/+Hffv1zmMsNShpG0K4nvvvsOBwcHqlSuQr9i/QhaHITxUSM142tmPIT0zTffoJTim/99c8f5582bl6M4AhYGsGroKgCM9ka6/dAN+xL2oKB09dK4tnC9zxlyJy4sjt96/EZkQCRP/9/TPP764xY9v7XkpXPndKVUU8zNNWeBly0RkBDCukpVLYWLpwuBxwI5fOAwv33+Gwv7L+SX7b+wZ+YennvuOVJvpNK3cV8WhS7i7f+8jSnFRLc63SzWLRLMc7ECtBjTgqe+ecpi581O1LEo5neaT8qNFAavH0ztp2rn6/UKUq6+h2it/bTW3dOXh2qtG2mtG2ute2aq1Qshiiht0qx+fjVRx6J4rNdjlHUtS2J8Ikkk4ezkTK1atUi4nMA8n3lcP3ed5iOa025SO5RSTJ8+nbZt2zJz5swHvn708Wh8J/jyY7MfOTD7AAD1etWzVPGypbVm7ci1mFJNvLjnRZtK8CDDGgghMPdW2fnZTg7+cpCroVfxnupNuw/bsf/N/fR8pydXuMK7Ld8l5mQMC7ss5PSF01yudZnx344HzN0ip0yZQmJiIj169KBp06a57jETHx3PnFZzSIpLolqbanT4rAN1nq5D5aaWnc3pdoFLAjm/5zw9fu5BxQYV8/Va1iBJXgjBtinb2PXZLmo/VZu2E9rSfFRztmzZQnh4OH9+9ycLBizg520/49DKgWup19heaztr1qzB0dHc6ySv3SLP7T7H2pFrSbyayMsHXqZS40r5Us7bJd9Ixne8L1UerULT4U0L5JoFrWjfNhZC5FnozlB2fbaLxkMa89zG5/B62Suj50zZsmUxGo044EDCtQSSHJPYVH0TcxbMyegSCeZukUBGt8h69XLexBKwKIC5beeSEp/CcxueK7AED7Bnxh7iwuLo8k0XDEbbTIe2WSohxH1prQn+I5hVQ1dRtlZZus7K2pXxySefxGQyMeSDIcxhDo/zOBFPRhAZHcm4cePw8fFhzpw5gLlbZKtWrWjVqhW1atW6Z7fIzFISUtj+0XYAXg14FffO7vc5wnKunrvK7i9202BQA2o8UaPArlvQpLlGiIeQNmlWPLuCo8uO4uTiRL/F/XAo5ZBlH4PBwLx584g8EskPjX+g9/zeNBnaJNvz5bRb5O0ClwQScyKGQasG4eDscP8DLMj3PfMTrk9Of7JAr1vQJMkL8RDa+397ObrsKE988AQ+U33u+cDPzeEC7Bwtmy6uR17Hb7IfLg1c8r0Hze1Cd4YSuCSQdpPbUbp6/oxeWVhIkhfiIbRp7CYA0lLS8J3gi07TaJP5pQwKg50Bg50BZVQkxCRY/PqBywJZMWgFAFUrV2Vp36XZ7qdQoDLeZPmpbi2ANt9Evf3bSLY0nP/nPCWrlszxJCNFmSR5IR5ie/+315zUjQaUQaEMCm3SmFJN5leaCZ2mMToYKVOjjEWuqbVm1ZBVGe8v+F8wD3FYwDp81sH8BK2NkyQvxENoip6S43211qC577ypOZEUl8SOT3dgSjHh0c/D3B6uuOsIklrrjOv3H96fgKMBjHp+FG+Pfpsz584wYswITp45yZLZS2jVvBUAhwIP8f5/3sfe3h6n4k78/PXPlCxRksNHD/P+x+8TGRCJdxVvJr8/Oc/lKQokyQsh7kmpTE0meXB662nWjlxL7JlYmjzfhJ5zeuaq2+L83+bj6+tLWFgY5euUp/gjxdm2Yxvjxo2jbI2yVKhfAYAfJv3AVzO/wtvbm6lTp7Jx30ZeffVVJo+YzFvt3iIoIIjVpVYTGxtL2bJl816wQk66UAoh8t2NSzdY8ewKdJpm0KpB9J7XO9f90rObfLtcuTvndW3QoEFGv/0rV65QsWJFkpKSiIuN48ycMzR9pikdnurAv//++8DlKUokyQsh8t26V9aRdDWJZ9c+S/3e9fP1Wv369ePNN9+kYcOG/Pvvv/Tq1Yvo6Gj0ZXOzT8dpHSlTpgzR0dH5GkdhIUleCJGvTvueJnhVMD4f+RTI06yvvPIKK1euJDAwkB49evD111+TcCqB6EvRtBzXkjI1ynD16tVsvwXYIknyQoh8o7Vmy/gtlK5R2uITfNzrmi4uLoB5qr7o6GjWPb8OB6MD1Z+rTkpKCrt27eLxx21jvPj7kRuvQoh8E308mosHL9JxWsc8P0w1cuRI9uzZQ1JSEv7+/syfP5++ffty7Ngxjh49SteuXfnoo4+YNm0aAwcOxNHREYPBwP+m/I+VX6ykd/neDB85HK01o0ePfihuuoIkeSFEPtrz5R6MDsa7DoeQGzmZfBvA29ubf/7559Y+E8z7vLPuHVxbWnY2qaJAmmuEEPkiJSGFQ3MP0ezFZpSqWsoqMcSciuGf//5D46GNH8oED5LkhRD55MjiI+g0jXuXghtZ8nZb39+Kwc5Ap2mdrBaDtUmSF0JYnDZptk3aRrU21ajbva5VYji/5zzHfj9G6/GtrfZNojCQJC+EsLjzf5/n+sXrePT1sMpkHFprNo3bRMkqJWn9TusCv35hIjdehRAWlRyfzNy2cwEoUakEJzaeuOf+N8fEyfzz5oiY2mR+gMlkMoEpfRwdzN8Ubrq5z839TWkmIvZHEL43nF5zez0Ug5DdiyR5IYRFLeu7LGM582iTBa1EpRI0GZb3Xj1FnSR5IYRFNXimAac2naLWk7UoX6f8vRuFTeYfGTX09FEnlVIZLwy3BklTBpUxYmWWkSvT91FGRdDvQcScjKHnLz0tMnJmUSdJXghhUR69Pfhr4l/maf1WDsK+ZME1l1y7cI19/7eP+n3qU7erdW74FjZy41UIYVGOZRwZsHwAsaGxbBm/pUCv/dekv0hLTrP5eVtzQ5K8EMLiqrepzmOvPYb/9/5cj7yeq2OPHz9OsWLF2LVrF9u3b6dNmzZ4e3vTvn17zp8/D8DYsWNp2bIlLVu2ZNq0aQBc2H+BQ/MO0WJMC8rVfjgGH8sJSfJCiHxxcxKP1MTUXB33n//8B29vbwBatWrF7t272b59O0OHDmXmzJkAvPbaa/zzzz/s2bOHP/74g5MnT7Jp7CZKuJTA+0NvyxakiJM2eSGExUUdi2LXZ7twaeBC6eqlc3zcvn37qFy5MkajEQB7+1vt+XFxcTRu3BiAOnXqAGAwGDAajeyZvodzu87RfXZ3HJxzMJn3Q0Rq8kIIi0iJT+HUllP8MeIPfmjyA2kpafT7rd9d52/NzieffML777+fZd369evx8vJi1qxZtGrVKsu2BQsWUL1Kdc78dAaAZi82y3tBbIzU5IUQeXJh/wX2frOXoFVBpNxIwc7RDq9XvWg7oS2lquR8OIGbybx8+fJZ1nfr1o1u3bqxbNkyJk6cyLJl5n74vr6+/Prrr7xR/w0OcYjntz1vladrCztJ8kKIB7bv231sfGMjDqUdaDS4ER59PajetvoDdZs8dOgQfn5+7NmzhyNHjhAcHMzSpUupUaMGAGXKlMHJyQmAvXv38uGHH7J+3XrmNpyLR18P3HzcLFk0m6FuPoRQELy8vLS/v3+BXU8IkX9MqSZmuMygXJ1yDPMdZtG28OHDh/PSSy8RHBzMggULMBgM2NvbM3v2bGrUqEHDhg0BKFWsFBGHIvji0y8YNHGQxa5f2Cil9mutvR7k2BzX5JVSRsAfCNdad8+0/h1gBuCitb78IEEIIYqeaxeukRibSJXmVSx+s3PevHkAtG3blpdeeumO7YGBgQBsGreJf4/9S+/Xe1v0+rYkNw1YY4CgzCuUUtWAJ4FzlgxKCFG4xUfHM897Hval7Hn0pUetEoPWmuBVwdR6spb0qLmHHCV5pZQr0A34+bZNXwPjgYJr8xFCWN26UeuIC49j6JahVG1e1SoxHF93nNizsdTvU98q1y8qclqT/wZzMjfdXKGU6om56ebwvQ5USo1SSvkrpfyjoqIeOFAhROFwwf8CQSuD8Jnqg2sL602pF7jY3GTj0dfDajEUBfdN8kqp7sAlrfX+TOucgA+Ayfc7Xms9W2vtpbX2cnFxyVOwQgjrMqWa2DtzL2DdPumpiakcX3ecZi81o3jZ4laLoyjIyY3XNkBPpVRXwBFwBhYANYHD6Q86uAIHlFKPa60v5lewQgjrWtJ7CSfWn6DF2BaUrFzSanGc2nKK5OvJePbztFoMRcV9k7zWegIwAUAp5QO8o7Xul3kfpdRZwEt61whh206sN8/yZOdgx5b3tphnY0rTd87YpM217fjL8Tg4O5jHib+5n741fvztbn86NmM/nXWfiIMR2Jeyp2aHmpYtoA2Sh6GEEDly5cyVjOXdX+y2YiRmFRtVxGhvtHYYhV6ukrzW2g/wy2a9m2XCEUIUVrFnYgFo8nwT3Lu4YzAaUEaFMqiswwmkLxoMBkwmE07lnTDYGcwzNxnMMz0NHj2YgOAARj43krdefotla5Yx97e5ONg7ULFCRf7v0//Dwd4h43xvfPAGFy9dZPlPywn7O4ylo5aypuQalndYjp2dHZs3by7Yf4wiRGryQogceeTxRwAoX7c8jZ5tlKdzLVi2AF9fX8LCwqjUsBLdnLox+r3RGI1Gxo8fz5bDWxgxYgQAR44cIYkk7EvYU6lhJQ79cohNhk38+r9fafpY07wWy+bJaD5CiByxc7TDztGOmJMxeT6Xq2vWrpe1atXKMrywnd2t+ufHH3/MxIkTAXMbfdDqIGJLxDLz+5l4e3sza9asPMdjyyTJCyFyxGBnwHOAJ4fnHyZoVdD9D3gAQUFBbNiwgUGDzOPQ+Pn5UbduXSpVqgTApSOXCDsTRtj1MMaMGcOWLVtYvHgxQUH5E48tkCQvhMixrt91pYRLCZb1XUZSXJJFzx0WFsbw4cNZvnw5jo6OAEybNo133303Y5/g1cEUpzhVqlShSZMm2Nvb4+Pjw5EjRywaiy2RNnkhRI45lHLAUMyAfUl7jA6W69ly+fJl+vXrx/fff4+7uzsA165d4+LFizzzzDMkJCRw9OhR/nvsv/Rs1RP3Yu6cP3+eatWqsX//fvr27WuxWGyNJHkhRK4Yixmp2LAidg4Pnj5GjhzJnj17SEpKwt/fH1dXV8LDwxk3bhwAQ4cOZcSIERw6dAiAs2fP8uLwF3l0+6PUfrU2/+vxP4YMGUJKSgodOnTg0UetM0haUSDjyQshcmXlkJWc23mOsaFjC/S6pzafYmGXhQzdMpRanWoV6LWtLS/jyUubvBAix0J3hBK8Opjy9crff2cLizgQAdzqyilyRpK8ECJHzvqdZdHTiyhdvTS9fulV4Ne/duEaDs4OMnZ8LkmSF0Lc15HfjrCg8wJK1yjN89uex9nVucBjuB5xnZJVrDcoWlElN16FEPd0Yf8F/njhD6q1qsagVYMoXs46Q/teu3CNUlVLWeXaRZnU5IUQd3X1/FV+8voJOwc7Bvw+wGoJHsxJ3vmRgv8GUdRJTV4IcVerh60GICkuiS8rfpk/F1H3365QaJOmSvMq+RODDZMkL4S4q4QrCQBUaloJYzGjOSHf1uv6jm7YOtP6e/TQzrw9Y1nfth7zsinVRMyJGMq6l81zmR42kuSFEHd1KfAStTrVYuiWoXk6z5QpU9iyZQv29vbMnDmTwMBAZs+eDUBkZCSenp6sWLGCtLQ03nvvPQ4dOkRqaiqzZs3C09OTi4cu8mOzHynjVsYCpXq4SJIXQmTrzF9n0Gka19Z5m6z70KFD7Nu3jz179nD+/HmGDRvGtm3bGDx4MACjR4+mXbt2AMyePZu6devy5ZdZm4ZuRN0AoEK9CnmK5WEkN16FEHc4t+scywcsp6x7WdqMb5Oncx0/fpzmzZsDUK1aNc6cOUNSknlws5SUFDZu3EivXuZ+98uXLyc0NJT27dvz+uuvk5ycDMCNS+YkX+oR6V2TW5LkhRBZBCwKYH7H+ThVcGLIpiHYl7DP0/kaNmyIn58fycnJHD58mLCwMK5cMU8luHHjRtq1a0fx4uZeO+Hh4VSpUoVt27bh6OjIL7/8AtxK8iUqlshTLA8jaa4RQmT4stKX3Lh0gxreNRi00jJ94j09PRk8eDBPPvkk7u7uNGjQABcXFwAWLlzIyJEjM/YtV64cTz31FABPPfUUK1euBMxJ3mBnwLGMY57jedhITV4IAUBcWFxGjXno5qEW7RM/evRotm/fzrhx42jUqBFGo5G4uDj2799Px44dM/bz8fHh5iCG/v7+1K5dG4CE6AQcyzqi1P36W4rbSU1eCAFAwMIAAF4JeAWjveXGigfo3LkzqamplC9fnu+++w6A33//nd69e2Mw3Kprjh8/nhdeeIEffviBcuXKsWDBAgBK1yhNfFQ8SXFJMnZNLkmSF0IAcH7PeYwORio1qmTxc2/evPmOdS+++OId68qWLcvq1avvWF+lmfkhqIuHL1LjiRoWj8+WSXONEIIrp69wcuNJmr7Q1NqhZKtys8rAreGGRc5JkhdCsPOznSijwvtDb2uHkq1SVUpRsnJJLh68aO1QihxJ8kI85OLC4jg45yBKqUI9ymPlZpWlJv8AJMkLm3Ls2DF8fHzw8fGhVatWlC9vnsFo/vz5dOzYkfbt27N48WIAVq1ahYeHB46OD3e3PGU091hJTUy9cxyaQqTKo1WIOhZFamKqtUMpUmSOV2Gzli1bxtKlS4mIiOD06dMMHTqUZs2aZYyZcuHCBTw9PQkMDCQkJCTbMVMeFnu+2sOWd7bg7OqMY7lsuiqqzIsq2/WZ1913n0zr77rvbcdFh0STdDWJYduGUdOn5l1OapvyMser9K4RNmv+/PlERkby5JNP0qRJEw4dOsTJkydZuHAhrq6uGWOmTJo06a5jpjwsmg5vyr7/28fV0KtcCrvEQhZixEgKKXSiE7UwT5x9gAOsYx2TmQyACRNb2MJFLmLCRDe6UZGK+Rrr2b/OPnRJPk+01gX2at68uRYiP2ydtFVPZarePWO31lrry5cva1dXV92rVy9dvXp17eLiordv367Xrl2rBw0apJOTk7Wbm5uOj4/X7u7uun379nrixInax8dHv/baazopKcnKJSp4S/st1VOZqkP3hOqUlBSttdanTp3SXl5eWmutExISdLdu3XTNmjUzjpk1a5b+8ccf73vuo0ePam9vb+3t7a1btmypy5Urp/38/HTr1q11u3bttI+Pjz537pzWWuvU1FT99ttv644dO2pvb2999OhRrbXWpjST/tz5c73u1XWWLnqhB/jrB8y70iYviqzUxFSigqLY+OZGdn6yE4At727hI/URS5YsoXnz5gQEBNCvXz/effddRo0aRefOnTly5EiOx0x5WOz8fCdBK4KwL2lP9VbVsbMzf8mPi4ujcePGAMycOZNXXnkly8NLdxtQ7Haenp74+fnh5+fHW2+9xYABA2jVqhW7d+9m+/btDB06lJkzZwK3RqL09fXFz88vo9lMGRSVm1aWHja5JEleFEk3om7wafFPmeU5i33/tw+Amh1vfYX/YfoPNK/WnJYtWtKlSxdOnTpFhQoV8PX1xd3dnYULFzJkyJCM/W8fMyUgIKBgC2Rlj41+DIDk68kcWXyE8PBw2rZtS+fOnenTpw9Xrlxhx44ddO/ePctxD/LhePPf3t7+1sBnmT9M7vXBUalpJSIDIjGlmSxR7IeCJHlRJJ3Zeuau62KIIfpcNDe+vYG/rz8dOnTAaDRy4MABPv/8cyZPnsz+/fuxt7enU6dOXLhwgYiICGbNmgVkHTPlYeFY2pGJ8RNxcnFi9fDVOFxzYNeuXezbt4/XX3+dzz//nPHjx99xXG4/HKOjowkODqZNG/PwxevXr8fLy4tZs2bRqlUr4N4fHFWaVSElPoWYEzGWKrrNy3GSV0oZlVIHlVLr0t//RykVoJQ6pJTarJSqmn9hCpFVw2ca8lbYW3T5ugvPb3sezwG3esKUoxwv8zLFKc5LQ16iffv2+Pv7s2jRInbu3ElAQAC9e/fG29sbX19f4uPjOXjwIKdPn8bHx4d9+/bx8ssvW7F0BS8xNpEvyn5BfFQ8ySnJXI+8DoCzszOlSpXi+PHjfPbZZzz11FNEREQwaNAg4O4Dit3N0qVLGTBgQEbvnW7duuHv788nn3zCxIkTgXt/cFRuan7y9eIhabLJqdz0rhkDBAE3p0ufobX+EEAp9SYwGXjFsuEJcXfOjzjTcmxLANx83Dix4QS+7/lyKfASYB7Uasx/xzBeZa2B5mbMlIfFF2W/yFgu2akkwz4chtFoJCUlhW+++SbLSJG1a9dm6dKlwN0HFLubRYsW8fPPPwOQmJiY8YxCmTJlcHJyAm59cNSuXfuOD44KHhVQRkXkkUgaPtPQMoW3cTlK8kopV6Ab8CkwDkBrHZdplxLcc8peIfJfna51qNO1jrXDKJL6L+3P74N+BwWdn+3Mu8PexWCX/Rf9kydPZizn5sPx9OnTJCUl4eHhAZjb5hcsWIDBYMDe3j7j+YV7fXDYOdhRoX4FLgVcesCSPnxy9DCUUup34HOgFPCO1rp7+vpPgWHAVaC91joqm2NHAaMAqlev3jw0NNRy0QshLOZG1A1+6/Eb4XvDcWvvxrCtwwrl+O0rBq/g/O7zjA0da+1QCkxeHoa6b5u8Uqo7cElrvf/2bVrrD7TW1YBFwOvZHa+1nq219tJae92cDUYIUfiUcCnBiL9H0Pzl5pzddpbIw5HWDilblZpU4uq5qyTGJlo7lCIhJzde2wA9lVJngSVAB6XUwtv2WQz0s3BsQogCppSiyfNNALh6/qqVo8lepcbm8e4jjxTOD6HC5r5JXms9QWvtqrV2A54B/tJaD1FKZW787AkE51OMQogCdHPav5s3sAubjCQfIEk+J/LST36aUipQKRUAdMbc+0YIUcTdTO52Dvk3tFWXLl1wcXHhk08+AczDq7zxxhs88cQTdO/enZgYcz/47EYKtStrx0K7hQz/z3BatmzJxo0b8y1OWyCjUAohski4ksD/av4Po72RNu+1waGUg3lkyXvdhM3B/dnMo01GxkSyN2gvl65c4qXuL7E7cDe+/r5MGT6FdX+v4/SF07zZ/01ir8dS3KE4AyYPYM3nawBISU1hw2cbqFq+Kn039aVNmzaEhITktdiFmoxCKYSwmOJli9N2Ylu2vreVLe9sybfrHOQgccSxZvUatrAFV1xZs2sNCSTgiy9uG90y9r3BDdaMWJPxXqG4eP4iDvYOWcbSEXeSJC+EuEObd9sQsT+CY8uOAVDDpwY129dEGe6ssue4NeC23RIOJhAVF4WPtw/+a/xp2aglzWo2w6RNLP52Md5v3JqKcPb/ZuM95tb7yEORBK8K5tURr2Y73IK4RZK8EOIOSikGLB1A1JQoVg1dRahfKGmJaQzfPhyjvdEi1zgz7wwlw0riPcmbjfEbcWvphndvb2JjY3nkz0fwmeyTsW/x+cWzvA/fF86Pq37ELcWNF154wSLx2Cr5niOEuCsXTxe6fN0FAFOaKduavCV4e3uzYcMGADZs2IC3970nFF++cznRRDO0ydB8iceWSE1eCHFPZ/3OAjB43eC7DnWQWyNHjmTPnj0kJSXh7+/PypUrWbduHU888QTOzs7Mnz8fgJ07d/LRRx9x4cIFOnXqxOjRo2nbti1vj38bt+JuvPbda1TcXpGtW7diNFrmG4atkZq8EOKeUpNSQWGxBA9QtWpVSpcujaurKx9//DEGg4EWLVpgb29PfHw8mzZtAmDr1q2Eh4fz+OOPk5qaSq9evahYsSJpaWlM7zWdUSVH4efnJwn+HqQmL4S4q/B94ez6bBdVvaqSeDWRpGtJGdsyd6m8oxkn/fPAYDBgsDNgsDdgZ2+Hwd5AQEAA+/btY9eOXZwPP8/w4cP59ttv8fX1xdfX946umh988EGWCV5uqtSkEoFLAkm4kkDxssUtV2gbI/3khRDZij4Rzbf1vrX4+LKBBBJJJB0xD1/8NV/TjGZc5zoxxGCPPU/zNKUpzTa2EUQQjjjSQDWgpWqZcR6tNWjos7APjZ9rbNkgCxnpJy+EsLhdn+8CDbU618K+pD3alCnbZ168raKYeT9t0uZXmsaUZkKnaYzXjPx64lecKzoTVzqOaweuYapoQqdqxtccz8FLB/GL8WNU7VF0S+7GADWA5LRkfjj/A3XK1aGOU52M68aciOHqucI5xk5hIUleCHEHU5qJQ3MPATB0k+V7sJSbVY6lS5fi7u5Ow9SGtO7aGnd3d1566SVSUlJo2rQprxzKOgeRwywHkpKSeP0t84C3Wmu+rPQlMSdlKsB7kRuvQog75Pc48qNHj2b79u2MGzeORo0aZZlGcP/+/bi7uwMQGxsLmBO6n58f9erVyxJjpUaVuHSkcA6kVlhITV4IcQdlULR6pxV/f/l3vpy/c+fOpKamUr58eb777jtcXFz4888/8fHxwWQyZcwSNXbsWEJCQtBa4+PjQ9euXbOcp2Kjihz46QDapPOtD39RJ0leCJEtnaYt9nTr7TZv3nzHuq+//vqOdfPmzbvneSo1rkRKfApXTl+hXO1ylgrPpkhzjRAiW0YHI1prcz/5Qqpio4oARByMsHIkhZfU5IUQ2arZoSa7p+1mernplKhUAp2WqdeMztrTJuO9vtW18ebPLPvfqzvmA7S2pCaYP4DObjtLgwENcn+Ch4AkeSFEtmp1qkXr8a25evYqhmLmh5qy3JDNnJQzjzd/cznTT8i6/Q7ZdMm8383fm/slXE6g2YvNclGyh4skeSFEtpRSPPnFk9YOQ+SRtMkLIYQNkyQvhBA2TJK8EELYMEnyQghhwyTJCyGEDZMkL4QQNkySvBBC2DBJ8kIIYcMKdGYopVQUEJqHU1QALlsonMLA1soDUqaiQspUNNwsUw2ttcuDnKBAk3xeKaX8H3QKrMLI1soDUqaiQspUNFiiTNJcI4QQNkySvBBC2LCiluRnWzsAC7O18oCUqaiQMhUNeS5TkWqTF0IIkTtFrSYvhBAiFyTJCyGEDSv0SV4p1UQp9bdS6ohSaq1Syjl9/ZNKqf3p6/crpTpYO9acukeZyiultimlriulvrV2nLlxtzKlb5uglDqplApRSnWxZpy5oZRqqpT6Ryl1SCnlr5R6PH29vVJqbnpZDyulfKwbac7do0zFlFK/ppcpSCk1wdqx5sQ9yvNc+rqbL5NSqqmVw82Ru5UpfVvj9P9nR9N/V473PaHWulC/gH8B7/TlF4H/pC83A6qmLzcEwq0dqwXKVAJoC7wCfGvtOC1UJk/gMOAA1AROAUZrx5vDMm0Gnk5f7gr4pS+/BsxNX64I7AcM1o43j2UaDCxJX3YCzgJu1o73Qctz2z6NgNPWjtUCvyM7IABokv6+fE7+LxX6mjxQD9iRvrwF6AegtT6otb6Qvv4o4KiUcrBCfA/ibmW6obXeBSRaK7A8yLZMQC/MySNJa30GOAk8ns3xhZEGbn4jKQ3c/HvzBLYCaK0vAbFAUXkI525l0kAJpZQdUBxIBuIKPrxcu1t5MnsW+K3AIsq7u5WpMxCgtT4MoLWO1lqn3e9kRWGO10CgJ/AHMACols0+/YCDWuukggwsD3JSpqLmbmV6BPgn035h6euKgrHAJqXUl5ibNlunrz8M9FJKLcFczubpP/dZI8hcGkv2Zfod8wdyBOaa/Fta6xirRJg7Y8m+PJkNwly2omIs2ZepLqCVUpsAF8yVp+n3O1mhSPJKKV+gcjabPsD81X+mUmoysAZzDSPzsQ2ALzB/yhUaeSlTYfWAZVLZ7F9o+u3ep0wdMSe7FUqpgcAcoBPwC+AB+GMei2kPkFowEd/fA5bpcSANqAqUBXYqpXy11qcLKOy7esDy3Dy2BRCvtQ4skGBz6AHLZIe5OfcxIB7YqpTar7Xees+LWbv9KZdtVXWBfZneuwLHgTbWjs1SZUpfN5wi1iZ/tzIBE4AJmbZtAlpZO8YcluMqt54lUUDcXfbbA3haO968lAn4Dhiaab9fgIHWjjevvyPga2CiteO00O/oGWBepv0+BN693/kKfZu8Uqpi+k8DMAn4If19GWA95gSy22oBPoC7lakou0eZ1gDPKKUclFI1gToUjWYNMLeFeqcvdwBOACilnJRSJdKXnwRStdbHrBNirmVbJuAc0EGZlQBaAsFWiC+37laem3+LA4AlVogrL+5Wpk1A4/S/P7v0fe7/d2ftT60cfKqNwVxbPw5M49Yn3CTgBnAo06uitePNS5nSt50FYoDrmNuvi0oN8V5l+gBzr5oQ0nsNFIUX5q/G+zG3we8Fmqevd0svSxDgi3kYWKvHm8cylQSWY+7EcIwc1BALw+tu5Unf5gP8Y+0YLVymIem/o0Bgek7OJ8MaCCGEDSv0zTVCCCEenCR5IYSwYZLkhRDChkmSF0IIGyZJXgghbJgkeSGEsGGS5IUQwob9PxDqk+rhVyaQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.  #skip for MO; all lakes interior\n",
    "lakeTracts = [-99999]\n",
    "minTractPop = 50\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if y < 48 and x > -96 : #leftover from another state\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x, y,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if y > 40: #don't worry about Atlantic ocean tracts\n",
    "            if m==761 or m ==676 or m==825: #trust me on this for Wisconsin\n",
    "                    thisTract = \"interior\"\n",
    "            else:\n",
    "                if lakeTracts == [-99999]:\n",
    "                    lakeTracts = [m]\n",
    "                    isSkippedTract[m] = 1\n",
    "                else:\n",
    "                    lakeTracts.append(m)\n",
    "                    isSkippedTract[m] = 1\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oceanTracts = [1094,1690,1239,607,785]\n",
    "for t in oceanTracts:\n",
    "    x,y = tractGeom[t].exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    isSkippedTract[t] = 1\n",
    "                \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1654 0\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "#isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "baabb633-83ac-4315-92bd-1d6fdd9e999b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, manually flag tracts that should be excluded from the map, such as lakeshore empty tracts\n",
    "isSkippedTract = [0]*nTracts\n",
    "#skipList = [1108,2676]   #Lake Erie and northeasternmost Susquehanna.  All others are small.\n",
    "for s in skipList:\n",
    "    isSkippedTract[s] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>NAME</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRECBLA</th>\n",
       "      <th>G20GOVRPAR</th>\n",
       "      <th>G20GOVDGAL</th>\n",
       "      <th>...</th>\n",
       "      <th>G20SOSRASH</th>\n",
       "      <th>G20SOSDFAL</th>\n",
       "      <th>G20SOSLFRE</th>\n",
       "      <th>G20SOSGLEH</th>\n",
       "      <th>G20SOSCVEN</th>\n",
       "      <th>G20TRERFIT</th>\n",
       "      <th>G20TREDENG</th>\n",
       "      <th>G20TRELKAS</th>\n",
       "      <th>G20TREGCIV</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>201</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>295</td>\n",
       "      <td>75</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>294</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>296</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>290</td>\n",
       "      <td>73</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((-89.44957 37.06921, -89.44957 37.068...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>043</td>\n",
       "      <td>West Finley</td>\n",
       "      <td>1646</td>\n",
       "      <td>528</td>\n",
       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1642</td>\n",
       "      <td>519</td>\n",
       "      <td>...</td>\n",
       "      <td>1664</td>\n",
       "      <td>437</td>\n",
       "      <td>54</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>1652</td>\n",
       "      <td>445</td>\n",
       "      <td>63</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((-93.26533 37.04093, -93.26521 37.040...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>175</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>549</td>\n",
       "      <td>128</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>518</td>\n",
       "      <td>153</td>\n",
       "      <td>...</td>\n",
       "      <td>546</td>\n",
       "      <td>115</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>543</td>\n",
       "      <td>122</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON ((-92.56115 39.42815, -92.56035 39.428...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29</td>\n",
       "      <td>165</td>\n",
       "      <td>Barry East</td>\n",
       "      <td>1206</td>\n",
       "      <td>1386</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>1237</td>\n",
       "      <td>1353</td>\n",
       "      <td>...</td>\n",
       "      <td>1334</td>\n",
       "      <td>1189</td>\n",
       "      <td>62</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>1268</td>\n",
       "      <td>1235</td>\n",
       "      <td>73</td>\n",
       "      <td>24</td>\n",
       "      <td>POLYGON ((-94.60068 39.26755, -94.60070 39.266...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>135</td>\n",
       "      <td>California 3</td>\n",
       "      <td>639</td>\n",
       "      <td>210</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>651</td>\n",
       "      <td>201</td>\n",
       "      <td>...</td>\n",
       "      <td>664</td>\n",
       "      <td>172</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>652</td>\n",
       "      <td>182</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-92.57752 38.62075, -92.57742 38.624...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP          NAME  G20PRERTRU  G20PREDBID  G20PRELJOR  \\\n",
       "0      29      201      Commerce         295          75           2   \n",
       "1      29      043   West Finley        1646         528          41   \n",
       "2      29      175    Huntsville         549         128           8   \n",
       "3      29      165    Barry East        1206        1386          42   \n",
       "4      29      135  California 3         639         210          18   \n",
       "\n",
       "   G20PREGHAW  G20PRECBLA  G20GOVRPAR  G20GOVDGAL  ...  G20SOSRASH  \\\n",
       "0           4           0         294          81  ...         296   \n",
       "1           4           2        1642         519  ...        1664   \n",
       "2           1           1         518         153  ...         546   \n",
       "3          15           7        1237        1353  ...        1334   \n",
       "4           3           1         651         201  ...         664   \n",
       "\n",
       "   G20SOSDFAL  G20SOSLFRE  G20SOSGLEH  G20SOSCVEN  G20TRERFIT  G20TREDENG  \\\n",
       "0          65           5           2           2         290          73   \n",
       "1         437          54          16          19        1652         445   \n",
       "2         115          12           6           5         543         122   \n",
       "3        1189          62          19          11        1268        1235   \n",
       "4         172          12           5           6         652         182   \n",
       "\n",
       "   G20TRELKAS  G20TREGCIV                                           geometry  \n",
       "0           8           2  POLYGON ((-89.44957 37.06921, -89.44957 37.068...  \n",
       "1          63           6  POLYGON ((-93.26533 37.04093, -93.26521 37.040...  \n",
       "2           6           7  POLYGON ((-92.56115 39.42815, -92.56035 39.428...  \n",
       "3          73          24  POLYGON ((-94.60068 39.26755, -94.60070 39.266...  \n",
       "4          19           3  POLYGON ((-92.57752 38.62075, -92.57742 38.624...  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/mo_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3733 0.5783582064440145 2971750 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic MO map\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-90.5,35.9)\n",
    "pt2 = Point(-89,35.9)\n",
    "pt3 = Point(-89.,37.3)\n",
    "pt4 = Point(-90.2,36.5)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip eastern outcropping NE of Philly\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  31 tracts w pop 88328.0  to reassign to tracts...\n",
      "[459, 993, 994, 1018, 1019, 1022, 1248, 1251, 1252, 1254, 1255, 1589]\n",
      "I have flagged  65 precincts to reassign to precincts...\n",
      "[0, 201, 213, 334, 389, 434, 438, 457, 532, 572, 607, 611, 796, 1035, 1099, 1310, 1437, 1474, 1476]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1  #uncomment once confirmed\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.2 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1  #uncomment once confirmed\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.2 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  88328.0 people (VAP of 66880.0 ) and  38527.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now work on the northern protuberance\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-95.,39.25)\n",
    "pt2 = Point(-94.5,40.61)\n",
    "pt3 = Point(-95.,40.61)\n",
    "pt4 = Point(-96,40.61)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "plotPoly(clipPoly)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  32 tracts w pop 91718.0  to reassign to tracts...\n",
      "[409, 416, 421, 428, 513, 1009, 1329, 1490]\n",
      "I have flagged  57 precincts to reassign to precincts...\n",
      "[29, 163, 164, 249, 270, 322, 352, 474, 519, 552, 659, 720, 764, 940, 962, 1052, 1055, 1103, 1280, 1284, 1440, 3676, 3678]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.15 :# and tractGeom[t].centroid.y > 48.3:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.15: # and vtdGeom[p].centroid.y > 48.3 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1 and vtdGeom[p].centroid.x > -123.4:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  91718.0 people (VAP of 71397.0 ) and  57255.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bfc91969-ca0c-4236-a020-ed826306f69c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for when I need to recover the whole map\n",
    "wholeMAP = tractMAP\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t]==1 :\n",
    "        wholeMAP = wholeMAP.union(tractGeom[t])\n",
    "plotPoly(wholeMAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.7642886942010847 2397\n",
      "100 0.6366336633663366 4040\n",
      "200 0.631598652190191 2671\n",
      "300 0.6140979689366786 837\n",
      "400 0.8514150943396226 848\n",
      "500 0.6730434782608695 575\n",
      "600 0.8709677419354839 186\n",
      "700 0.848404255319149 376\n",
      "800 0.8122448979591836 735\n",
      "900 0.7725988700564972 708\n",
      "1000 0.0 0\n",
      "1100 0.7728459530026109 1149\n",
      "1200 0.7704918032786885 427\n",
      "1300 0.4855421686746988 830\n",
      "1400 0.0 0\n",
      "1500 0.6320582877959927 549\n",
      "1600 0.172090112640801 1598\n",
      "1700 0.6249201277955272 1565\n",
      "1800 0.35787321063394684 2934\n",
      "1900 0.3783783783783784 37\n",
      "2000 0.3471337579617834 314\n",
      "2100 0.373015873015873 378\n",
      "2200 0.16725352112676056 568\n",
      "2300 0.28450704225352114 355\n",
      "2400 0.5696902654867256 904\n",
      "2500 0.6123642439431913 1197\n",
      "2600 0.0 0\n",
      "2700 0.43252595155709345 289\n",
      "2800 0.07913669064748201 278\n",
      "2900 0.0 0\n",
      "3000 0.0 0\n",
      "3100 0.0 0\n",
      "3200 0.2394705174488568 831\n",
      "3300 0.47368421052631576 627\n",
      "3400 0.431438127090301 299\n",
      "3500 0.7777777777777778 9\n",
      "3600 0.0 0\n",
      "3700 0.7718120805369127 1788\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%200 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "plotPoly(wholeMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 18.61495216849305 17.344017347081564\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "plotPoly(tractMAP)\n",
    "plotPoly(origMAP)\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  6154899 2971728 0.5783582064440145\n",
      "compare to Census 6154913 Leip has Trump + Biden = 2971750 0.5783582064440145\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 6154913\n",
    "atlasTrump = 1718736\n",
    "atlasBiden = 1253014\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 6154899 6154899.0\n",
      "state Trumpers before, after slice opn are 1718726 1718726.0\n",
      "state Bideners before, after slice opn are 1253002 1253002.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 35 grids for 8 districts\n",
      "starting grid no 0 at x = -94.6893477142857, y = 36.9087198 \n",
      "starting grid no 5 at x = -93.86226714285714, y = 36.9087198 \n",
      "starting grid no 10 at x = -93.03518657142857, y = 36.9087198 \n",
      "starting grid no 15 at x = -92.20810599999997, y = 36.9087198 \n",
      "starting grid no 20 at x = -91.38102542857143, y = 36.9087198 \n",
      "starting grid no 25 at x = -90.55394485714285, y = 36.9087198 \n",
      "starting grid no 30 at x = -89.72686428571427, y = 36.9087198 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 282459.89207001415 2898420.2204671795 32.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 8   #MO congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2.5  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.2849230025361318e-08 2.4729216499974452e-05\n",
      "MO 8 minTractPop = 10\n"
     ]
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "print(STATE,nDistricts,\"minTractPop =\",minTractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 5 3 198917.3328350494 2.0242\n",
      "8 yoyos for tract,wedge,wedgePop,r= 5 3 68524.73636571896 1.0121\n",
      "9 yoyos for tract,wedge,wedgePop,r= 5 3 256108.1271549337 2.1474\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5 3 96668.02635917021 1.5797\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5 3 133841.96705718528 1.8635\n",
      "10 yoyos for tract,wedge,wedgePop,r= 5 3 186348.21252717305 2.0055\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5 3 233111.59888820033 2.0764\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5 3 212243.7533862154 2.0409\n",
      "11 yoyos for tract,wedge,wedgePop,r= 5 3 198154.1857660959 2.0232\n",
      "I am working on tract number 20 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 31 1 1275849.7910110913 4.3825\n",
      "we have 2 non-opposing shorted wedges for tract no 38\n",
      "we have 2 non-opposing shorted wedges for tract no 39\n",
      "I am working on tract number 40 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 56 3.0 3 36.0 0.9674 783957.6\n",
      "I am working on tract number 60 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 63 3 1544283.284089515 3.261\n",
      "8 yoyos for tract,wedge,wedgePop,r= 63 3 109324.21818945347 1.6305\n",
      "9 yoyos for tract,wedge,wedgePop,r= 63 3 1683827.1298741312 3.678\n",
      "9 yoyos for tract,wedge,wedgePop,r= 63 3 1107704.4763406445 2.6542\n",
      "10 yoyos for tract,wedge,wedgePop,r= 63 3 215983.25730337948 2.1424\n",
      "10 yoyos for tract,wedge,wedgePop,r= 63 3 537238.917247707 2.3983\n",
      "11 yoyos for tract,wedge,wedgePop,r= 63 3 888117.2165660745 2.5263\n",
      "11 yoyos for tract,wedge,wedgePop,r= 63 3 703520.5655560996 2.4623\n",
      "12 yoyos for tract,wedge,wedgePop,r= 63 3 613125.4089549447 2.4303\n",
      "12 yoyos for tract,wedge,wedgePop,r= 63 3 655942.0071729001 2.4463\n",
      "7 yoyos for tract,wedge,wedgePop,r= 64 3 1509069.425414065 3.1754\n",
      "8 yoyos for tract,wedge,wedgePop,r= 64 3 82967.06268203654 1.5877\n",
      "9 yoyos for tract,wedge,wedgePop,r= 64 3 1610144.549659427 3.4229\n",
      "9 yoyos for tract,wedge,wedgePop,r= 64 3 1046925.6489400079 2.5053\n",
      "10 yoyos for tract,wedge,wedgePop,r= 64 3 191542.02861971327 2.0465\n",
      "10 yoyos for tract,wedge,wedgePop,r= 64 3 516798.62431008596 2.2759\n",
      "11 yoyos for tract,wedge,wedgePop,r= 64 3 852352.2662494667 2.3906\n",
      "11 yoyos for tract,wedge,wedgePop,r= 64 3 683818.6034533087 2.3332\n",
      "12 yoyos for tract,wedge,wedgePop,r= 64 3 595449.224625272 2.3046\n",
      "12 yoyos for tract,wedge,wedgePop,r= 64 3 637680.8865343188 2.3189\n",
      "12 yoyos for tract,wedge,wedgePop,r= 64 3 660927.909687011 2.3261\n",
      "I am working on tract number 80 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 82\n",
      "we have 2 non-opposing shorted wedges for tract no 92\n",
      "we have 2 non-opposing shorted wedges for tract no 94\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 98 3.0 3 36.0 1.4251 288403.3\n",
      "I am working on tract number 100 of 1654 tracts\n",
      "I am working on tract number 120 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 128\n",
      "we have 2 non-opposing shorted wedges for tract no 129\n",
      "I am working on tract number 140 of 1654 tracts\n",
      "I am working on tract number 160 of 1654 tracts\n",
      "I am working on tract number 180 of 1654 tracts\n",
      "I am working on tract number 200 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 210 3 457937.3513947489 2.7165\n",
      "I am working on tract number 220 of 1654 tracts\n",
      "I am working on tract number 240 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 250\n",
      "I am working on tract number 260 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 267\n",
      "I am working on tract number 280 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 287 1 821847.2901622744 2.6756\n",
      "8 yoyos for tract,wedge,wedgePop,r= 287 1 65392.578809120925 1.3378\n",
      "9 yoyos for tract,wedge,wedgePop,r= 287 1 821839.180037864 2.6756\n",
      "9 yoyos for tract,wedge,wedgePop,r= 287 1 507364.42937709833 2.0067\n",
      "10 yoyos for tract,wedge,wedgePop,r= 287 1 112620.49145828263 1.6723\n",
      "10 yoyos for tract,wedge,wedgePop,r= 287 1 229860.32284390065 1.8395\n",
      "11 yoyos for tract,wedge,wedgePop,r= 287 1 383890.1099425897 1.9231\n",
      "12 yoyos for tract,wedge,wedgePop,r= 287 1 300513.7432532351 1.8813\n",
      "we have 2 non-opposing shorted wedges for tract no 298\n",
      "we have 2 non-opposing shorted wedges for tract no 299\n",
      "I am working on tract number 300 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 312\n",
      "I am working on tract number 320 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 329\n",
      "7 yoyos for tract,wedge,wedgePop,r= 339 1 1377472.6308407388 2.8656\n",
      "I am working on tract number 340 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 340\n",
      "I am working on tract number 360 of 1654 tracts\n",
      "I am working on tract number 380 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 398\n",
      "I am working on tract number 400 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 406\n",
      "we have 2 non-opposing shorted wedges for tract no 409\n",
      "we have 2 non-opposing shorted wedges for tract no 410\n",
      "I am working on tract number 420 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 436 6.0 0 90.0 1.2338 213001.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 436 7.0 0 90.0 1.1418 213001.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 436 8.0 0 90.0 1.0566 213001.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 436 9.0 0 90.0 0.9778 213001.0\n",
      "we have 2 non-opposing shorted wedges for tract no 439\n",
      "I am working on tract number 440 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 440\n",
      "we have 2 non-opposing shorted wedges for tract no 441\n",
      "I am working on tract number 460 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 460\n",
      "we have 2 non-opposing shorted wedges for tract no 461\n",
      "I am working on tract number 480 of 1654 tracts\n",
      "I am working on tract number 500 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 501 1 1121074.7003088465 2.9429\n",
      "8 yoyos for tract,wedge,wedgePop,r= 501 1 50167.60773827019 1.4715\n",
      "9 yoyos for tract,wedge,wedgePop,r= 501 1 1122132.172922783 2.9464\n",
      "9 yoyos for tract,wedge,wedgePop,r= 501 1 980971.505221043 2.209\n",
      "10 yoyos for tract,wedge,wedgePop,r= 501 1 210138.9806433532 1.8402\n",
      "11 yoyos for tract,wedge,wedgePop,r= 501 1 545307.6683106672 2.0246\n",
      "11 yoyos for tract,wedge,wedgePop,r= 501 1 358755.33261859673 1.9324\n",
      "12 yoyos for tract,wedge,wedgePop,r= 501 1 272641.2510029473 1.8863\n",
      "12 yoyos for tract,wedge,wedgePop,r= 501 1 313212.50806424586 1.9093\n",
      "I am working on tract number 520 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 527\n",
      "we have 2 non-opposing shorted wedges for tract no 528\n",
      "I am working on tract number 540 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 543 5.0 0 90.0 1.7805 290345.9\n",
      "we have 2 non-opposing shorted wedges for tract no 559\n",
      "I am working on tract number 560 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 560\n",
      "I am working on tract number 580 of 1654 tracts\n",
      "I am working on tract number 600 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 601 4.0 2 90.0 2.7196 229162.2\n",
      "I am working on tract number 620 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 622 7.0 3 90.0 1.7324 295850.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 622 8.0 3 90.0 1.6072 295850.0\n",
      "I am working on tract number 640 of 1654 tracts\n",
      "I am working on tract number 660 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 661\n",
      "7 yoyos for tract,wedge,wedgePop,r= 668 3 1245468.9979631344 2.2352\n",
      "8 yoyos for tract,wedge,wedgePop,r= 668 3 4521.677685904782 1.1176\n",
      "9 yoyos for tract,wedge,wedgePop,r= 668 3 1863903.3358569415 3.2692\n",
      "9 yoyos for tract,wedge,wedgePop,r= 668 3 1031816.0811966029 2.1934\n",
      "10 yoyos for tract,wedge,wedgePop,r= 668 3 28221.973679863615 1.6555\n",
      "10 yoyos for tract,wedge,wedgePop,r= 668 3 238471.97430897396 1.9244\n",
      "10 yoyos for tract,wedge,wedgePop,r= 668 3 488055.64776699326 2.0589\n",
      "we have 2 non-opposing shorted wedges for tract no 670\n",
      "I am working on tract number 680 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 682 3.0 1 90.0 2.0896 206342.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 682 4.0 1 90.0 1.9814 206342.0\n",
      "we have 2 non-opposing shorted wedges for tract no 688\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 692 4.0 0 90.0 0.6219 285086.5\n",
      "I am working on tract number 700 of 1654 tracts\n",
      "I am working on tract number 720 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 738 3.0 2 90.0 2.1142 235879.1\n",
      "I am working on tract number 740 of 1654 tracts\n",
      "I am working on tract number 760 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 762 0 503095.90324921656 0.8225\n",
      "8 yoyos for tract,wedge,wedgePop,r= 762 0 39622.15665282699 0.4113\n",
      "9 yoyos for tract,wedge,wedgePop,r= 762 0 503680.0191403024 0.8259\n",
      "9 yoyos for tract,wedge,wedgePop,r= 762 0 405424.10547193704 0.6186\n",
      "10 yoyos for tract,wedge,wedgePop,r= 762 0 151463.65006365316 0.5149\n",
      "11 yoyos for tract,wedge,wedgePop,r= 762 0 289146.9350008005 0.5668\n",
      "11 yoyos for tract,wedge,wedgePop,r= 762 0 216718.07521522298 0.5408\n",
      "12 yoyos for tract,wedge,wedgePop,r= 762 0 181816.0169287347 0.5279\n",
      "13 yoyos for tract,wedge,wedgePop,r= 762 0 198405.41270828206 0.5344\n",
      "7 yoyos for tract,wedge,wedgePop,r= 775 1 578929.5770508244 2.4418\n",
      "8 yoyos for tract,wedge,wedgePop,r= 775 1 57953.833049119916 1.2209\n",
      "9 yoyos for tract,wedge,wedgePop,r= 775 1 711947.7565727114 2.6251\n",
      "10 yoyos for tract,wedge,wedgePop,r= 775 1 128422.14243248024 1.923\n",
      "10 yoyos for tract,wedge,wedgePop,r= 775 1 322911.0833528752 2.2741\n",
      "11 yoyos for tract,wedge,wedgePop,r= 775 1 590090.3603332192 2.4496\n",
      "11 yoyos for tract,wedge,wedgePop,r= 775 1 482373.7725943775 2.3618\n",
      "11 yoyos for tract,wedge,wedgePop,r= 775 1 403591.1875031946 2.318\n",
      "11 yoyos for tract,wedge,wedgePop,r= 775 1 361530.66439030645 2.296\n",
      "I am working on tract number 780 of 1654 tracts\n",
      "I am working on tract number 800 of 1654 tracts\n",
      "I am working on tract number 820 of 1654 tracts\n",
      "I am working on tract number 840 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 843\n",
      "7 yoyos for tract,wedge,wedgePop,r= 846 3 1536968.5587604048 3.8835\n",
      "we have 2 non-opposing shorted wedges for tract no 855\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 858 5.0 3 90.0 1.7663 222468.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 858 6.0 3 90.0 1.5803 222468.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 858 13.0 0 90.0 1.6534 558347.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 859 5.0 3 90.0 1.7016 237125.7\n",
      "I am working on tract number 860 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 874 4.0 0 90.0 3.3609 199677.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 874 5.0 0 90.0 3.2674 199677.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 874 6.0 0 90.0 3.1766 199677.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 874 7.0 0 90.0 3.0882 199677.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 874 8.0 0 90.0 3.0023 199677.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 876 6.0 0 90.0 0.9486 205513.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 876 7.0 0 90.0 0.9022 205513.0\n",
      "I am working on tract number 880 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 885\n",
      "we have 2 non-opposing shorted wedges for tract no 887\n",
      "we have 2 non-opposing shorted wedges for tract no 888\n",
      "we have 2 non-opposing shorted wedges for tract no 894\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 898 5.0 2 90.0 3.8829 205991.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 898 6.0 2 90.0 3.6866 205991.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 898 7.0 2 90.0 3.5002 205991.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 898 2 205574.5085176451 3.4129\n",
      "8 yoyos for tract,wedge,wedgePop,r= 898 2 79726.24814082783 1.7064\n",
      "9 yoyos for tract,wedge,wedgePop,r= 898 2 205572.19581924126 3.4124\n",
      "10 yoyos for tract,wedge,wedgePop,r= 898 2 101443.56979838299 2.5594\n",
      "10 yoyos for tract,wedge,wedgePop,r= 898 2 152812.54071313472 2.9859\n",
      "I am working on tract number 900 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 902\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 904 5.0 3 36.0 2.7376 473462.3\n",
      "I am working on tract number 920 of 1654 tracts\n",
      "I am working on tract number 940 of 1654 tracts\n",
      "I am working on tract number 960 of 1654 tracts\n",
      "I am working on tract number 980 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 993\n",
      "I am working on tract number 1000 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1003\n",
      "we have 2 non-opposing shorted wedges for tract no 1011\n",
      "we have 2 non-opposing shorted wedges for tract no 1019\n",
      "I am working on tract number 1020 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1021\n",
      "we have 2 non-opposing shorted wedges for tract no 1022\n",
      "I am working on tract number 1040 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1047 15.0 1 90.0 2.7901 230203.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1047 1 230203.242603168 3.2726\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1057 6.0 1 90.0 1.6272 243522.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1057 11.0 1 90.0 1.6271 243522.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1057 1 243522.52115465543 1.7639\n",
      "I am working on tract number 1060 of 1654 tracts\n",
      "I am working on tract number 1080 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1080\n",
      "I am working on tract number 1100 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1102\n",
      "I am working on tract number 1120 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1133 0 276834.228244261 2.2865\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1133 0 37777.34450772416 1.1432\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1133 0 277319.81945365615 2.3165\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1133 0 98806.05714195262 1.7299\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1133 0 246629.5022024772 2.0232\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1133 0 154640.15375611643 1.8765\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1133 0 203088.90363326753 1.9498\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1133 0 181871.7365934266 1.9132\n",
      "I am working on tract number 1140 of 1654 tracts\n",
      "I am working on tract number 1160 of 1654 tracts\n",
      "I am working on tract number 1180 of 1654 tracts\n",
      "I am working on tract number 1200 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1202 1 391646.1065566402 0.9916\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1202 1 16498.159007045848 0.4958\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1202 1 631200.5604889954 1.0933\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1202 1 96049.62641261029 0.7946\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1202 1 270641.74319994875 0.944\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1202 1 482956.1083377149 1.0187\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1202 1 362662.2732156032 0.9813\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1202 1 312965.97048214264 0.9626\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1202 1 337516.63407703734 0.972\n",
      "I am working on tract number 1220 of 1654 tracts\n",
      "I am working on tract number 1240 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1249\n",
      "we have 2 non-opposing shorted wedges for tract no 1250\n",
      "we have 2 non-opposing shorted wedges for tract no 1254\n",
      "we have 2 non-opposing shorted wedges for tract no 1259\n",
      "I am working on tract number 1260 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1266\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1279 3 579125.496221288 2.101\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1279 3 53192.38506119349 1.0505\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1279 3 622252.3588509111 2.1955\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1279 3 135091.01705897803 1.623\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1279 3 414954.19301660504 1.9092\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1279 3 198014.91254186977 1.7661\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1279 3 275542.3358326204 1.8377\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1279 3 340870.2584032255 1.8735\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1279 3 378555.4522709116 1.8914\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1279 3 359843.0116906382 1.8824\n",
      "I am working on tract number 1280 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1281 3 528639.8758681586 1.9732\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1281 3 33825.4402961678 0.9866\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1281 3 579927.8621128572 2.0903\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1281 3 107103.20783796097 1.5384\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1281 3 333010.62480160105 1.8144\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1281 3 517489.9623715115 1.9523\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1281 3 453824.34103282914 1.8833\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1281 3 403066.6940343083 1.8488\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1281 3 368770.0745549402 1.8316\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1281 3 351357.1367104818 1.823\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1281 3 360052.65909716766 1.8273\n",
      "we have 2 non-opposing shorted wedges for tract no 1295\n",
      "we have 2 non-opposing shorted wedges for tract no 1296\n",
      "we have 2 non-opposing shorted wedges for tract no 1297\n",
      "we have 2 non-opposing shorted wedges for tract no 1298\n",
      "I am working on tract number 1300 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1300\n",
      "we have 2 non-opposing shorted wedges for tract no 1301\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1313 2.0 0 90.0 0.3258 204568.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1313 3.0 0 90.0 0.311 204568.0\n",
      "I am working on tract number 1320 of 1654 tracts\n",
      "I am working on tract number 1340 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1356\n",
      "I am working on tract number 1360 of 1654 tracts\n",
      "I am working on tract number 1380 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1385 2.0 2 90.0 1.2586 288573.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1385 3.0 2 90.0 0.912 288573.9\n",
      "I am working on tract number 1400 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1402 5.0 1 66.8 2.798 458063.4\n",
      "I am working on tract number 1420 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1422 1 434305.0219057987 2.3315\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1422 1 63093.34105494601 1.1658\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1422 1 657317.5658198136 2.4913\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1422 1 125743.33235592593 1.8285\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1422 1 182358.3663725216 2.1599\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1422 1 420263.06790337304 2.3256\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1422 1 257078.4603689727 2.2428\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1422 1 330583.01372329984 2.2842\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1422 1 377094.7765101014 2.3049\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1422 1 355094.88384302665 2.2945\n",
      "loop31.0, tr1422,wedgePops775603.29, 23812.0, 367436.4, 23638.3, 360716.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01410 \n",
      "   targetWP, latest drx4 are tWP,dr, 360965.8,0.3009, 360965.8,0.0052, 23618.7,-0.0002, 360965.8,0.0004\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1422 1 367436.4018964007 2.2997\n",
      "loop32.0, tr1422,wedgePops769614.82, 23812.0, 361447.9, 23638.3, 360716.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01510 \n",
      "   targetWP, latest drx4 are tWP,dr, 360965.8,0.3009, 360965.8,-0.0026, 23618.7,-0.0002, 360965.8,0.0004\n",
      "we have 2 non-opposing shorted wedges for tract no 1427\n",
      "we have 2 non-opposing shorted wedges for tract no 1432\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1434 1 1876769.493687579 2.1068\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1434 1 35571.44867910654 1.0534\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1434 1 1945798.7713913894 2.7608\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1434 1 1268960.825010809 1.9071\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1434 1 115547.90893345862 1.4802\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1434 1 486809.1165238083 1.6937\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1434 1 946256.1433588925 1.8004\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1434 1 749500.2915699331 1.747\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1434 1 627476.9050946321 1.7204\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1434 1 552872.3789301468 1.707\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1434 1 589975.9190074503 1.7137\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1434 1 608712.6397947157 1.717\n",
      "we have 2 non-opposing shorted wedges for tract no 1435\n",
      "we have 2 non-opposing shorted wedges for tract no 1436\n",
      "I am working on tract number 1440 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1441\n",
      "we have 2 non-opposing shorted wedges for tract no 1443\n",
      "we have 2 non-opposing shorted wedges for tract no 1459\n",
      "I am working on tract number 1460 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1475 2.0 1 90.0 1.4088 221280.7\n",
      "we have 2 non-opposing shorted wedges for tract no 1479\n",
      "I am working on tract number 1480 of 1654 tracts\n",
      "I am working on tract number 1500 of 1654 tracts\n",
      "I am working on tract number 1520 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1529\n",
      "we have 2 non-opposing shorted wedges for tract no 1534\n",
      "I am working on tract number 1540 of 1654 tracts\n",
      "I am working on tract number 1560 of 1654 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1578 6.0 1 90.0 3.8338 237500.9\n",
      "I am working on tract number 1580 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1587\n",
      "I am working on tract number 1600 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1604\n",
      "I am working on tract number 1620 of 1654 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1621 3 1472785.3740063563 2.9273\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1621 3 79836.9651078058 1.4637\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1621 3 1627627.9321764766 3.4183\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1621 3 932682.3463886327 2.441\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1621 3 136693.63441487262 1.9523\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1621 3 323609.7601777455 2.1967\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1621 3 572726.4760971133 2.3188\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1621 3 755752.8262385859 2.3799\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1621 3 660936.4687278752 2.3494\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1621 3 708860.8508940899 2.3646\n",
      "we have 2 non-opposing shorted wedges for tract no 1623\n",
      "we have 2 non-opposing shorted wedges for tract no 1625\n",
      "I am working on tract number 1640 of 1654 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1650\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0000642128057664\n",
      "Average and max number of wedgePop loops per tract were:  7.929262394195889 32.0\n",
      "min,average,max of Home District areas were:  0.0 2.208410253624908 6.1931208407508285\n",
      "calculated statewide vote was 0.5828340876673322, should have been 0.5783582064440145\n",
      "calcd statewide Hispanic pop was 0.03908177033688185, should have been 0.04128601737327069\n",
      "calcd statewide Black pop was 0.09612014884126914, should have been 0.10849960172643841\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.5374848851269649\n"
     ]
    }
   ],
   "source": [
    "#3/7 - 3/10/22 - linear wideAngle, short the opposing wedgePop to match first shorted wedge (see nonper toy)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #NEW 3/7/22\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opposite wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                #now, we \"squish the square\" based on how close we are to boundary - see offline documentation\n",
    "                equivL = (4.*minWedgePop[t] / avgDistrictPop)**0.5  #non-dimensional centroid-boundary distance\n",
    "                wideAngle = avgWedgeAngle*max(  1,( 1.+(maxAngleRatio-1.)*(levelL - equivL)/(levelL - 0.) )  )\n",
    "                wideAngle = min(maxWideAngle, wideAngle)  #prevent too wide of an angle (half-pie)\n",
    "                #HERE, WE ADJUST all wedge ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit to avoid gaping wedges\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                # NEW 3/10/22 - we not only squish the angles, we short the target for the wedge opposite boundary\n",
    "                oppWedgeExpectedPop = minWedgePop[t] * math.tan(0.5*wideAngle)/math.tan(pi/nWedges)  #prediction for new wedge0's pop\n",
    "                oppWedgeExpectedPop = min(oppWedgeExpectedPop,avgDistrictPop/nWedges) #not needed, but just in case...\n",
    "                for nW in range(nWedges):\n",
    "                    if nW == int(nWedges/2):\n",
    "                        targetWedgePop[nW] = oppWedgeExpectedPop\n",
    "                    else:  #NOTE: we don't mess w wedge0's target.  When it shorts again, we will adjust other wedge's tgts\n",
    "                        targetWedgePop[nW] =(avgDistrictPop - oppWedgeExpectedPop)/float(nWedges-1)\n",
    "                # *** END OF 3/10/22 MODIFICATION\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0000642128057664\n",
      "Average and max number of wedgePop loops per tract were:  7.929262394195889 32.0\n",
      "min,average,max of Home District areas were:  0.0 2.208410253624908 6.1931208407508285 for  MO\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start MO 8:58, end 10:13 with some other bkg work going\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MO 19Mar 8\n"
     ]
    }
   ],
   "source": [
    "date = \"19Mar\"\n",
    "print(STATE, date, nDistricts)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"maxWideAngle\",\"levelL\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, maxWideAngle, levelL]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.5664245704782598 2397 pop,GOP 0.7642886942010847\n",
      "175 0.6628066582926274 218 pop,GOP 0.9403669724770642\n",
      "213 0.5785677172297458 4384 pop,GOP 0.78125\n",
      "226 0.6417172185023413 1059 pop,GOP 0.8356940509915014\n",
      "265 0.6807627563173411 1838 pop,GOP 0.8394994559303591\n",
      "286 0.6697066451803549 1760 pop,GOP 0.58125\n",
      "293 0.6157387763398887 1391 pop,GOP 0.30481667864845435\n",
      "334 0.606776832643553 2949 pop,GOP 0.7900983384198034\n",
      "362 0.6997918397388656 747 pop,GOP 0.6693440428380187\n",
      "367 0.6268218048439669 2014 pop,GOP 0.48212512413108244\n",
      "399 0.6903159939563278 1979 pop,GOP 0.8489135927235978\n",
      "438 0.5759950677351898 2119 pop,GOP 0.728645587541293\n",
      "457 0.6268000751962637 2241 pop,GOP 0.7728692547969657\n",
      "469 0.6923313053710204 316 pop,GOP 0.8544303797468354\n",
      "572 0.601079155250549 2440 pop,GOP 0.7770491803278688\n",
      "611 0.5943169204523916 2483 pop,GOP 0.7752718485702779\n",
      "766 0.6282391215351667 516 pop,GOP 0.45930232558139533\n",
      "796 0.6543058445539378 3099 pop,GOP 0.80864795095192\n",
      "833 0.6974114549533363 403 pop,GOP 0.4466501240694789\n",
      "902 0.6562249251506825 504 pop,GOP 0.7797619047619048\n",
      "1035 0.5673566518058267 3226 pop,GOP 0.7994420334779914\n",
      "1169 0.6996764582712054 1401 pop,GOP 0.582441113490364\n",
      "1284 5.246184112835097e-11 2854 pop,GOP 0.6867554309740714\n",
      "1285 0.6814622661308828 1751 pop,GOP 0.5985151342090235\n",
      "1291 0.6608035882160215 432 pop,GOP 0.5208333333333334\n",
      "1310 0.6843590593539693 2134 pop,GOP 0.7685098406747891\n",
      "1437 0.6717968644586608 2745 pop,GOP 0.7872495446265938\n",
      "1476 0.6596740486278568 6866 pop,GOP 0.8195455869501893\n",
      "2203 0.6892485440356872 544 pop,GOP 0.10661764705882353\n",
      "2214 0.6783731172926634 246 pop,GOP 0.12195121951219512\n",
      "2215 0.6836454025835074 20 pop,GOP 0.15\n",
      "2218 0.6830276034685553 315 pop,GOP 0.13968253968253969\n",
      "2233 0.6659329667981121 695 pop,GOP 0.20863309352517986\n",
      "2729 0.683404823628045 105 pop,GOP 0.20952380952380953\n",
      "2733 0.6813515586767084 625 pop,GOP 0.2352\n",
      "2737 0.6975395356567868 469 pop,GOP 0.1513859275053305\n",
      "2738 0.6944263828399976 1124 pop,GOP 0.152135231316726\n",
      "2739 0.6836585290396594 250 pop,GOP 0.244\n",
      "2748 0.6934114638893845 606 pop,GOP 0.14026402640264027\n",
      "2750 0.6964066490238137 1 pop,GOP 0.0\n",
      "2779 0.6992970041122745 584 pop,GOP 0.1934931506849315\n",
      "2781 0.6764616130126514 838 pop,GOP 0.23269689737470167\n",
      "2788 0.6903525205752353 940 pop,GOP 0.17446808510638298\n",
      "3087 0.6912036597455529 300 pop,GOP 0.49666666666666665\n",
      "3095 0.6879859807302271 222 pop,GOP 0.42792792792792794\n",
      "3109 0.6798905490503477 1 pop,GOP 0.0\n",
      "3113 0.6744882628808814 3 pop,GOP 0.0\n",
      "3658 0.6836784071747252 1 pop,GOP 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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T62qFU2v7UltaPNOCjZ9s5IOwD1j47EKS9ycbHUsUk9yJWQJZZ7KY2nEqJ+NP0undTkT1iiLnbA5hzcLkjL5wSonrE1n/4Xp2/bQLu81Ozbtq0uX9LvhX9Tc6mqDoOzGlgJeQPd/OxOiJnIw/Wbis3WvtiHk1xrhQQlyn9GPpbPx0I+vGrsPsaqbpE01p92o7zC4y2I+R5Fb6UmaymLhn9j20fv7CTPNph9MMTCTE9fMO9abDGx14ePXDhDUNY9Wbq/ik5ies/3g9eVl5RscTfyEF/Dr4R/rT+JHGANTqW4uuH3Q1OJEQpSOkUQgPLnyQ++bch2clTxaMWMDnjT7nxNYTRkcTF5ECfp08K3kCEFg3EDc/N4PTCFG6onpFMeT3ITwU+xC5GblMjJ7Iz//8Wa5YKSekgF8nV29XIjtEsvb9tWz+crNMaixuSlU7VeWx+MdoPqI526ZtY3r36eSczTE61i1PCngpuHva3QTWCmTOo3MYe9tYds7YaXQkIUqdR0UPun7QlX7f9SNxbSJftfpK7ocwmBTwUuAd4s0jGx7h4VUP4xfpx6wHZpF5KtPoWEKUiToD6nDvL/eSnZrN93d9T9bpLKMj3bKkgJcSpRQRbSIIbhiMPd/O/oX7ObrhqNGxhCgTNXrUoN+3/bDl2jiy5ojRcW5ZUsBLWeU7KqPMitkPzebL5l+ybdo2wDHNVeL6RA6vOozdZjc4pRDXL6x5GCaLicS1iUZHuWXJeOClrOGghtToWYMze86w6LlFzB44m62TtpKZlEnSjiQAoh+PpsXTLUhLSCO8RThWTxmmVjgfi6sF71BvUg+lGh3lliUt8DLgGehJROsIBi4eSMtnW5JzNgf3AHd6TexFQPUA4ibEMf728XzT6RsmtZlkdFwhSiy0aSgJvyeg7TImkBGkBV6GrF5WurzX5ZJl1TpX44/Zf5CbnsvyV5eTky6XYgnnVbt/bf6Y+QdrxqyhzfNtjI5zy5EW+A3mV8WPls+0xC/SD4Ben/UyNpAQ16HOgDrU6luLZf9dJo0RA0gBN4i2Of7kDKgeYHASIUpO2zX5Oflom8aWYzM6zi3nqgVcKeWmlNqglIpXSu1USo0qWN5AKbVWKbVdKTVHKeVT9nFvHuf7DGWSZOHMFj67kL1z99J5TGc8KnoYHeeWU5wWeA7QQWvdAGgIdFNKtQC+BF7QWtcDZgMjyyzlTUgKuLgZnD1yFoD6D9Y3OMmt6aoFXDucnwjSpeChgduBlQXLY4F+ZZLwJiUFXNwMOr7dEWVWrHh9hdFRbknF6gNXSpmVUluBJCBWa70e2AHcWbBJf+C2It47VCkVp5SKO3XqVClEvjlIARc3g4o1K9JoSCM2fbapWFOxabvGni83spWWYhVwrbVNa90QCAeaKaXqAoOBfymlNgHewGWH4dNaT9RaR2utowMDA0sptvOTAi5uFs2ebIY9386h5YeuuN3v7//OaP/RvOnxJh9Hfcx3d37Hlq+3kJ2a/bdtH3nkEdzc3DCbzURFRbF58+ZL1nfv3h0PDw8sFgvVqlVj6dKlfPDBB7i7u2OxWPDy8mL06NEAdOrUiUqVKqGUonbt2qX2ucuDa55STSn1KpCptX7vomVRwDStdbMrvfdmmlLtem0Yv4H5w+cz8tRIOfkjnJot18aHlT8koEYAg1YMuuy8sCfiT/B5w8+p2qkqIU1CSNmfwvHNx0k5kILZ1UxUzyjq3l+XsKZhDHl6CDNnz7zk/R4eHjRs2JCPP/6Ye+65h337LoyC6O3tjY+PD0ePXjr2kNlsJiwsjMTEROz2C63+6OholFLce++9PPvss6X8bZSNoqZUu+qNPEqpQCBPa52qlHIHOgGjlVJBWuskpZQJeBn4rNRT38SkBS5uFmarmTteuYN5T8xj+WvLiXkt5pIinpaQxi8P/4LF3cLd0+7Gq5IX4Bgf6NjGY2z/djs7vt/BH7P+IIUUZuIo3p5unuTZ88jNzSUvL4/MzEyaNWuGzea4XNFkMmE2mwkJCeHAgQOFxzOZTIUF+9ixY5cUbwBXV1fMZjMvvvgiU6ZMoUKFCoSHhzN16tQy/Z7KQnHuxAwBpiilzDi6XGZorX9TSj2llPpXwTazALkn/BpIARc3k8aPNCbx90RW/m8l586co82LbfAJ8+HQikP82P9H8rPz6f9j/8LiDY4RPMOahRHWLIwu73Xh8MrDjBk3BpffXMiz5eGX7ccJHFO45eXlER8ff8kx7XY7drudffv2XVKkzz8/X+j/6uDBg5w4cQK73c62bdv45ptvePDBB0v7K7khrlrAtdbbgEaXWf4R8FFZhLoVSAEXNxOzi5k+U/vg4unCxk82EjchDr8qfqQcTKFCVAXu/fleKtasWOT7TRYTVdpXIWNYLMqWh8lkomL1iiTtScLG5QuxxWLBZrP9rYV9NceOHbvk9euvv467uzv9+jnfhXRyJ6ZBpICLm41Sil6f9WL4vuG0+U8bQqNDaf+/9jy64dErFu/zjgx+H7/UVKzK0QVy5MQR7OYLxbkKVS7Z3svLi6u5XH/8X40YMYJXX32VlJSUq25b3shgVgaRAi5uVgHVAujweodres+eo8ng8x69vH34/PRpAFLOXlpQj1mOYco3YcdR1FNTU6+63+JcpHH06FEaNmzI3r17adbsitdhlDvSAjeIFHAhLjiTlcvv/g1Y0ctK5bq+l2055+bn4oJLsfZ3tZa3r68vZrMZgF27drFr1y4qV6587cENJgXcIFLAhbigZY1g7nzmZ/5x1+f8GZ+M3W5nzOT/8Nhb31DztmoAWLBgcXd0Gnh4eDBixAi2bNlSuI8qVaoUPg8PDwcclxKaTKbC5xUrOrpy0tLSCk9yzp07l86dO1OpUqUy/5ylTQq4QaSAC3GpAF93GnW8A1VQcGd696RS7D66JjiGXM4nn8xzjsnC27Rpw8SJE4mNjaV3794AHDp0CHBcJrhhwwZcXFwuOclZvXp1nnvuOSyWS3uOX3755cKbfpyN9IEbRAq4EFc2cP4RkpYpbqtVgUHNBvHTzJ9QSjF+/HhcXV2xWCyMHDmSYcOG0ahRI5KTkzGZTCxbtozg4GCmT5/O4MGDiYqKIiwsjG+//RYvLy927dpFQkICiYmJPPbYYzz33HNGf9QSu+Y7Ma+H3Il5wfLXlrNi1Apesb9SrDPlQtxq3q3wLoG1Axm08vJ3d95KiroTU7pQDKLtGlTxLnMS4lZUvVt1EtclcnT90atvfIuSAm4QbdfSfSLEFfT4pAc+t/nw6yO/Gh2l3JICbhAp4EJcmZufGw0fbsipnadkvs0iSAE3iN1mx2SWr1+IKznz5xlcfVxx8Sje9d+3GqkgBpEWuBBFy8vKI/b/Ytk+fTtNhjWRxk4R5DJCg0gBF+Ly7DY7P9z9A/sX7afx0Ma0f7290ZHKLSngBpECLsTlLfnPEvYv2k/PCT2JfuxvV86Ji8jfJQaRAi7E3yWuT+T3d3+nyWNNpHgXgxRwg0gBF+Lvtny1BRdPF7qM6WJ0FKcgBdwgUsCFuJTdZmf3z7u5vfftWL2sRsdxClLADSIFXIhLHV1/lKxTWUTdGWV0FKchBdwgUsCFuCDrTBZLX16KycVE9W7VjY7jNOQqFINIARfCIfVwKpPbTSb9WDq9PuuFu7+70ZGchhRwg0gBFwIyTmYwtcNUctJyGLx6MGHNwoyO5FSkC8UodhkLXIi1H6wl9XAqDyx4QIp3CUgBN4i0wMWtLj8nn61fb+X2O28nvHm40XGckhRwg0gBF7e6xHWJZJ3OosHABkZHcVpSwA0iBVzc6vKz8wHwCPQwOInzkgJuECng4lbnGegJQPrRdIOTOC8p4AaRAi5udUF1g3DxdOHQikNGR3FaUsANIgVc3OrMVjNV2lVh96zdZJ3OMjqOU5ICbhAp4EJAzKgYzqWcY0r7KRxZfcQx2bcoNingBpECLgSERody/9z7ST2UyqS2k/iswWec2nXK6FhOQwq4QaSAC+FQtWNVnkl8hj5T+5B5KpOpnaZy9uhZo2M5BSngBpECLsQFbr5uNHioAQ/FPkRuei7f3/k9uZm5Rscq96SAG8Rus6PMUsCFuFilepXo930/Tmw9wcQmE9k7f6/Rkco1KeAGkRa4EJcX1TOK++fdDxq+7fEtE6MnMq3bNPYt3Ictz2Z0vHLlqgVcKeWmlNqglIpXSu1USo0qWN5QKbVOKbVVKRWnlGpW9nFvHlLAhSha9a7VeXz743R+rzP55/LZv3A/07tNZ1rXaUZHK1eK0wLPATporRsADYFuSqkWwLvAKK11Q+CVgteimKSAC3FlZquZVs+14omdT/BS9ksAHFp2iE1fbOLgsoPk5+QbnNB4Vy3g2iGj4KVLwUMXPHwKlvsCx8ok4U1KCrgQxWdxtTBk3RACawfy29DfmNphKl80/YKEtQlGRzNUsfrAlVJmpdRWIAmI1VqvB54GxiilEoD3gBeLeO/Qgi6WuFOn5PrO86SAC3FtwpuH8/iOxwu7Vs6dOcfkOyaz+avNnEs5R8LaBE5uP2l0zBtKaV38O5+UUn7AbGA4MBRYobWeqZQaAAzVWne60vujo6N1XFzcdcS9eUy6YxImi4l/Lv2n0VGEcErZqdn82P9HDiw+cMnynhN6Ev1YtEGpyoZSapPW+m8f6pquQtFapwLLgW7AP4FZBat+BOQk5jWQFrgQ18fNz437591P7y96X7LcxcPFoEQ3XnGuQgksaHmjlHIHOgG7cfR5tyvYrAMgF2xeAyngQlw/s4uZWn1rgXKc9Hx8x+OFE0Qc33ycpS8vJXF9osEpy05xJjUOAaYopcw4Cv4MrfVvSqlU4COllAXIxtGlIopJCrgQpcM9wJ0GDzVg27RtnNh6As9AT7ZN28bi5xdjz7ez6s1V3H7n7dz59Z14VLi5Jo+4agHXWm8DGl1m+WqgSVmEuhVIARei9HQf353jW44z+8HZhctq9KhBr897sXHCRla/tZp5T8yj7cttCawdiMl8c9zDWJwWuCgDUsCFKD2u3q4MjRvK/tj9JO9NJrhhMJXbVUYpRfv/tWfPnD3snLGTnTN24l7Bnb7T+1K9a3WjY183KeAGkQIuROkyW81E9Yz623KT2cSjGx7l9O7TnNx2ktXvrGZ6t+nU6luLdq+2o1L9SgakLR03x98RTkgKuBA3jsXNQnDDYBoMbMDQTUO54793cGDJAT5v9LlTD5glBdwgUsCFMIaLuwvt/9eepw48BQp2/rDT6EglJl0oBpECLoSxXDxc0HZN/JR4jm08RpUOVeg8urNTXUcuLXCDaLtGKSngQhjF4mah56c9qd6tOqd2nWLj+I380PcHMpMyjY5WbNICF0LcsqIfiy687X79x+tZMGIB71V6j6C6QUT1jqLdq+2wuJbfMll+k93klFJcyzg0Qoiy1Xx4cyJaR7Bv4T72/LqH1W+vJqBGAI0e/tttMOWGFHCjKBwD8gohyo2QxiGENA6hctvKTGo7CY+K5fvOTekDN4gySQtciPLK5OIojQm/l+/xxqWAG0QphbZLAReiPAqNDqV2/9qseWcNK15fYXScIkkBN4p0oQhRbp3aeYq0w2kALH91OfnZ5XP6NukDN4icxBSi/Dqw5ABHNxzFv6o/de6tg9nVbHSky5ICbhRpgQtRbnkGegLQ/8f+hDQOMThN0aQLxSDSAhei/KrauSquPq4seGpBuT5XJQXcIFrLnZhClFdelbzoOrYrR1YfIf6beAAmT55Mq1ataN26NZs3b/7be0aPHk3Hjh2JiYlh6dKll6xr164djzzyCACJiYnExMTQtm1bWrduzfXMEyxdKEbRyFgoQpRjDf7ZgPHvjOfOwXcSMDqAbHs227Zt4+jRozz00EOsXr2a0aNHs2jRIk6dOkXdunVZsmQJ8+fP5/nnn8fNzY2IiAgGDBiAj48PK1euJCQkhM6dOzNjxgyCgoLYtWsXw4YNY9WqVSXKKAXcINquHf3gQohyacJnE5h2eBo2u42jfxxFmRU7duygcePGHDx4kLp165KYmEhmZiZ2u53jx4/TqFEjdu3ahYeHB3a7nTVr1rB48WKmTJnCF198wf79+4mNjWXAgAGsWbOGdevWYbGUvAxLF4pBpAtFiPJrY7Pm/HfECPJyc7Fhw44dm83GwAcfAqB69epkZmaSmZlJfn4+drsdrTWDBw/GxcWFjIwMsrKy0Fpz7tw5nn/+eebPn8+OHTs4ceIEqampBAYG8tJLL/HSSy+VOKcUcKNopAUuRDk1ds+fpNjtf7tQbO+fe3n33XdZv349hw8fJj//wvXhJpOJSZMm4eHhQX5+PjabDYDg4GDGjBlDjRo1MJlMKKXw9vYmKiqKnj170qlTpxLnlAJuEGmBC1E+paSksCg7G28XF5RSeHp64u7mDoDdbqd169b4+voW/v+rlMJqtXLmzBkOHjxIcnIytWvXxmq1ApCWlsaLL77Izp07MZvNWK1WVq9ejZeXF8OHD7+urFLAjSItcCHKpfnDh1MJyNegNWSfO8e57HMA5GNjxIgRVK1atfAyYK11YTdKWloaNpsNNzc3cnJyAMc/CH369MHHx4fs7OzCVntsbCytW7emf//+Jc4qBdwg0gIXovzJmdCeo0uXEm51Jd9uRwE2u/2Sbf7xj39QpUoVzGbH3ZkeHh5YLBZcXV0JDg7GYrGwfft2lFKFLfiRI0cSEBAAgM1mIzQ0FE9PT9577z1+/PHHEueVAm4UaYELUa5MnjyZ9m+t4sMsOOlfD+wm9GVulx4/fjyJiYmFLemsrCxyc3PJycmhVatWBAQEkJeXh9YarTV2u50WLVpw6NChwn0EBQWRk5Nz3TfzSQE3iLTAhSg/Vq68nzffHMbYd0P5Z6eX+eNoHK64YlKOEqmUwmw2ExgYSI8ePahYsSIWiwWTyYSLiwseHh6YTCaWL1+O1WotPFkJUK1aNZ566ikALBYLSim2bdtGXl4eJtP1lWAp4EaRFrgQ5caa35dRr54bpvz2VMg5iM2eTxZZuGpXXF1cUSjq1KlDRkYGc+fOJTY2trDrxNvbm/r16+Ph4UFKSgpJSUk0atSISpUqAY47L9esWcPIkSPx9PTE1dWVGjVq0LJlS0JDQ68rtxRwg0gLXIjyIe3UOY4ldsTL28RJ6za+2jqdChX8qN6oCj6uPgTkBeCCC/Xq1cPf35+srCzy8/O58847mTFjBnl5eWzatAkXF8ds9udb41FRUVSsWJGpU6eyatUq1q1bR05ODnv27GHZsmUA/Otf/6JDhw4MHDiwRNnlTkwjSf0WwjBJh88yZ1w82Zl5xMXvZ8eOfBYuOEJ4uA/ZBzNx8/IiIzCbrKNZ5OgcfvrpJ/Lz82natCkbNmzgu+++Y9q0aZhMJux2OxaLBU9PT1544QVmz57NypUrcXV15YsvviA8PJxTp06htaZevXqcPXuWFi1aMHHiREJCSj7aobTAhRC3nF2rj/Hj23FkZ+aRlZPOoQOH8fbyoVH7puzZdxpvD29O7jnJqZOnuL1STaxY8fH2ISwsjAoVKtCuXTu01litVr766iv8/Pxo0aIFubm5vPHGGxw4cABfX1/q16/Pjh07SEpK4uTJk/Tt25dFixbRtm1brFYrTz31FDExMcycObNEn0Na4EKIW0rqySwWf7sVm/UcbvkBnDy3n5pBLQirUI3ly2eQmZ6Ft5sf6Rnp5OXlsTltC1HUYOfpXYDm+PHjNG7cGLvdTm5uLg8//DBKKVauXEmdOnWIj48nIyOj8GRlTk4OFosFPz8/cnJyePPNN4mJieHDDz9k165deHt706pVKzp06IC/v/81fRZpgQshbhlffPEFH054l+SgDaQFbOdk0ApOJifg5uZOy5rdGdThTbTWBPV6DUvDO8FkoW79aAJqVyWIQDzdHa3wdevWERISwmOPPUZSUhIAd911F5s2bcJms9GzZ0+io6OpX78+Li4uBAcHM3bsWLp06cLq1au56667qFevHmFhYfj4+NCwYUP27t17zZ9HWuBGKr/jxAtxU7rcyH/70texffcO1p7YiqlmW5SLKztmvowtIxm0nT/+3Iu/u5kUlYrpnInDhw4XXoAwdepUoqKicHNzK+wTB5g7dy4AUVFR2Gw2jh07xrBhw/Dx8aFOnTo0aNCArKws0tPTcXd3Z9euXVSuXPmaP4+0wA0iV6AIcePFxMQUPm/evDltojtx+GAC2rMiHt1GcHbjz7i5exHq60vs0jgwmcnNSmfgwIE0aNyI6v4NcMEFHw9/YmJiGDlyJG+88Qb5+fnUrl2biIiIwv27u7tz6NAhQkJCCA8P57bbbsPDw4Pnn38es9nM6NGj6d69O61bt+bRRx8tvOzwWkgLXAhxy4iMjKRNmzasXr2a48ePExqqiGnXimXpQSTP+xAyk6nbuDHVq1Vly/oFKGXCnpfD6DHvoVzccfELpjKRnNGprFmzhhUrVqC15jbv28g7kEdkw0iCgoKIj4/HarVitVqpVq0ap0+fJj8/n4yMDLp27QpA586d6dy583V9nqu2wJVSbkqpDUqpeKXUTqXUqILlPyilthY8Dimltl5XEiGEKEN5eXnMmTOHM2fOAJBqTuWbnydz4mgioQ3uIPih9zBXqs7mP/Zz6CykBDcBswWzlz9hj08i4I4HqB5Rl7704cvHJ/Hkk0/ia/alJjUZkj6EAekDGOg2kEOHDuHr68s777xDaGgoa9asITMzE6vViqenJ+np6aX2mYrTAs8BOmitM5RSLsBqpdR8rfU95zdQSr0PpJVaKiGEKGUpKSls2rSp8PXZg2dRGRay8u3cad9ISPoRnsrNwlqjJeuXL2Dz1q24hkSRe/oIkckb+eLjZ+nZvTvfu23h2w++xepmJdAjkEB7IL9n/M4e9mCPtZNuSqd79+489thjtG/fniFDhtCpUyeqV6/OggUL2Lt3L82aNSuVz3TVFrh2yCh46VLwKDz9phyduQOA70olkRBClIGgoCAqNaxEsjWZdJd0ciw5uISe4Oie3bgd2MmRI6cJ1mf5v4gcalXyIv1gPK8M7c9tFbxY9e04tm7ZTM9ePYk7GMfT/3oaDw8PAs4GEOodSscKHRnmO4yHhz6Ml/ai8mHHCcnzM/c899xz3HvvvSU+WVmUYvWBK6XMwCagOvCJ1nr9RavbAie11pe9BkYpNRQYClzSwS8uNXnyZCZOnIhSio8//pjGjRtfsv785Kk2m41XXnmFDh06sHbtWp577jksFgu9e/dm5MiRBqUXwjkMu2sYDdIaADCtxzQemvsblt7evLNiJbfZ8/iihzc5ejvbbq9VeLNOnTp1aNq0KWlpaaxcuZKxH4/lnQnv4O7ujru/O0mnkwjLC6Pfe/0YOWskdg87Xju8mHnfTBLXJVLvUD2aV2+OdxXvEp+sLIq6luEMlVJ+wGxguNZ6R8GyCcA+rfX7V3t/dHS0jouLK2HUm8v428cT0jiEft/1IyUlhY4dO7Ju3bpLZrw+b/78+axatYq33nrrkn00bdqUmTNnEhERQc+ePRk7dixRUVE3+qMI4VR6zOpBQnoCTzR8gk+3fkqQexBLBiwp8f7s+XZyzubgHuCYtSflQAqzH5pNWkIaYU3D0Fqze/ZuHl79MBGtS9aIVUpt0lpH/3X5NV1GqLVOBZYD3Qp2agH6Aj+UKNUt7Gx2HnGHkxnx3RaGvPstNeo3xWq1EhkZSUZGRuFsHgAzZswgOzubjh078tBDD5GW5jjdkJaWVvhXTXR0NMuXLzfiowjhVCZ1nQTAp1s/BcDF7HJd+zNZTIXFG8C/qj+D1wzmmSPPMGDmAKq0rwKAZ5DndR3nsse+2gZKqcCCljdKKXegE7C7YHUnYLfWOrHUk93k0s7lkXQ2h22JqazaeYiEzAvXhfv6+pKcnFz4+tixY5hMJpYsWULz5s15++23AahYsSLx8fHk5uayePHiS94jhLi8Sp6V2PzgZr7v9T3fdP+Gqd2nlunxdv24i8DagQRUDyj1fRenBR4CLFNKbQM2ArFa698K1t2LnLwssUo+biwf2R5vXz9s2ZmFy9PS0gqnXwIICAigW7duAHTr1o1t27YBjtuCn3/+eXr37k3VqlWve2xhIZzV5MmTadWqFa1bt2bz5s1/Wz969Gg6duxITEwMS5cuxcXsQqR7JOP/M577et9HTEwMKSkppZ4r5WAKR1Ydoe79dcvk5r2rnsTUWm8DGhWxblBpB7oVqaAaHF33HV999RWffvopBw4cYOfOnYUnMmNiYoiLi2PTpk188803pKamsnTpUjp06MCrr77Ks88+y8aNG6latSrgGED+wQcfxGazYbfb+eijj4iO/lv3mRA3hZSUFMaNG3fFc0hpaWksWXJpP/eoUaMYMGAAXbp0KZNcmacyWfTcIsxWMw0ealAmx5Bb6Y1ScO7YZtfYXDxp3vVunnrqKaxWK9988w1DhgxhzJgxAAwaNIjY2Fg+/fRTQkNDiYuLo0OHDnzwwQeFd3VNmzaNuLg49uzZg7e3NzNmzGDVqlV88cUXPPPMM0Z9SiHKRE5ODunp6WRmZrJq1Spat26N2WymSpUqxT6HtGTJEhYsWEBMTAyvvvpqqeZb/MJi3gt6j92zd9NoSCN8I3xLdf/nSQE3ktZk5TomRg2rHMmQIUNYu3Ytd911F1prRowYAYCrqysRERH069cPm83GyJEjSUtL49lnnyU4OJi1a9fSo0ePwhOZvr6+BAUFAWC1Wi87gI8QzionJ4f333+f999/nzFjxjBlyhS2b9/O66+/zqhRo0hPT+f48eOF2xd1Dmn79u106NCBZcuWsWvXLhYsWFBqGf/85U+8Q70ZuGQg3T/uXmr7/Ssp4AbRClCQmWMDYOfvS5g1a1ZhH95fT2SuWbOGn376CZvNhr+/P2+//TZZWVmkpaXRrFkz7rjjDhYsWEBycjKdOnUiJiaGJk2a0K9fP1566SWDPqUQpS87O5vc3FxCQkIKB4MKDAykffv2uLu7k5mZiY+PT+H2RZ1DOr9cKUXXrl0Ll5eG4EbBpB9LZ/ELi5n35DxyM3JLbd8Xk6aZQbzPpeP352E2dP+JV2yal3f8TteqkTw9cCD/GjGCjIyMwhOZ8+fPx2Kx8PXXX9OpUyf27dvHiBEjGDVqFK+//jqzZs1Ca03lypUJDQ1l3rx5WK1WBg4cyNy5c+nUqZPBn1aIkomPj2fLli34+PgUngQ8deoUAMHBwTRv3pyoqCi+/fZbWrVqxcmTJ7FarZcU8PPnkDp16kRcXBzVq1e/ZHmLFi2Ii4sr1b7w2v+ozY7vdnBs4zGObTxGUJ0gmj1ZOrfPX0wKuEE8bLm4ahu5Hl7sOZ5A84CKbNx/AI8VK0hOTsbPzw9XV1fA0YcXGhrKsGHDaNWqFe3bt6d69eosWbKEvLw8srOzadOmDVu2bKF79+5YrVZGjhxJhQoVaNmypcGfVIiSmz17duFzX19HP/L5PmwvL6/Cdf369aN58+ZkZWXRq1cvduzYQWxsLCNHjmTQoEE8+uijtG/fHhcXF6ZOdVw2+M477/Doo4+SnZ1NjRo16NOnT6lk3vjpRub9a94ly4LqBZXKvv/qmu7EvF5yJ+YFYz3+jb/HWfqNq8OPm/ewPy0X999+Y1ZODvttNn744QeCg4OJjY1l8eLF1KpVi+TkZNatW0deXh5r166lcuXKPPTQQ+zfv5+dO3fy5JNP8sorr7B+/XpatmyJ2WymatWq1K9fnx9//NHojyzENTty5Ahff/01AMOHD6dChQqAY2RBi8XCjh07mD17Nna7vfA9/v7+PPXUU4bkBfik1idou2bwmsFknHAMIxVU9/oKeFF3YkoL3DB27Ln5JP7vM/IzM0jIzOTFoEr0A+5zc6V9+/a4urrSsGFDNm/eTK9evS7pPgkODiYgIIDPPvsMi8XCl19+Wdhn3rx5c+x2O2fOnKFp06Z8+eWXxn5UIa7AnmtDmRXKfOGU3MVjA919991kZGTw8ccf07x5c86ePcvcuXNZv349OTk59O3bl0GDBjFq1CiOHj2KxWLhtddeY+rUqTRq1OiGX1IbcUcEmyduJu6zOO54+Y4yPZa0wA0yvuLTBIZkcefLESQvGkurORDo5onN4oL28Wbr1q2F237++ef8/PPP5Obmcvz4cW6//XZmz55NZGQkLi4uhIaGkpCQwJgxY+jduzeHDx/mmWeeIT09na1bt3Ly5MnC7hghypPP/jOWL6d8hVKK/3V6ivYv9uVckCocG+i///0vn3/+OX5+frRr146aNWuyaNEi4uLiCAwMJDw8nMWLF2O1WgFITk6matWquLi4kJCQQE5ODjk5OQQFBbFr1y6GDRvGqlWryvQz2fPtfNXqK45tPMbQTUMJaRxy3fsslbFQRCkyWzD7+OHWpDUeFfPAxQVLaCgmfz8Atm7dWngdeEhICImJidjtdsLDw5kwYQIATZo0wdfXF7vdTkxMDH369CEpKYlmzZpx5swZcnNzmTBhghRvUS6lpKQw4YcvmXH/OCY8+S6vLh5H2vyD/L5iDW1atiZ2wSJMykSVKlXYvXs3U6ZM4cUXX2T79u3cc889REZGcvToUX799dfCfb799ttUq1aNOnXq4ObmVqaX1GanZWPLtf1tuT3fTnZqNgCndp0qteNdjhRwg+xzDeFn1yjCj3gzLTmMPl1jWLduXeGA87Vq1SocHnb27Nl07twZk8lEpUqVcHd3DJzj5eWFUgqtNV26dMFkMpGfn0/9+vUJDw8vnNJJiPJo/fr1tKzdFFdPd5qM7EqWyiEjMZUDX2/AEp/BlJc+5fSSAxzcvZ++DbpxeJrjFnmr1UpeXh6LFy/Gy8uLOXPmAI7+8uPHj5OVlUWrVq0uOZbNZuPJJ58slUtqDyw5wCe1P2G032jGRozl9J+nL1m/4OkFJO9NJmZUDPUeqHfdx7sSKeAGsykLEwJ64R18YZD34g5m9d5777FhwwZ++eUX3nnnHQ4cOMCxY8fYsmULY8eO5ddff+XVV18tkzEehLhWednZHNi8kQObN3Jo6yb2xG/Bw+yCzs4neckBKlSphG5fgfB2UWSHmDnjnolJKcJ9g2kWXpePFjlOZtasWZM5c+ZQs2ZNwsLCCi8rHDVqFMOHD+fkyZPUrFnzkmMPGzaMnj17lsoltb8O/pW8rDxi/heDtmm+v/N7ziWfc3zGc3lsm7aNRkMa0e6VdmU+ebkUcINYTYowVxdeqhrC8YAqLDh64V/x4g5mVbFixcL1nTt3Jj4+noCAAOrVq0dYWBg+Pj40bNiQvXsvO9eGEDdUfOw8Zo8exezRo5j59qtsnzubg/v/AODc0uOkpZ/ltk61iBnckw0Ht1IxMpi6laLwdHGnW6vO7D59AHDcQTl06FD+/PNPcnNz0Vqzfft2lFLEx8dTq1YtTKYLpW3kyJGEhIQwfPjwq2bMzchlzrA5fFL7E6Z1ncZvj//GoRWHCtef+uMUaUfSaPlcS9r9tx33zL6H1EOpTI6ZzL6F+1j68lLyMvOoM6BO6X55RZACbrDhlStRP7opB+M2kJeXx5EjR/Dy8rqk3/r8DQfAJTcipKamApCbm8uaNWuIioqievXqZGVlkZ6eTn5+fqlP4SRESWVnZqBMJu5/833u/d8YRowey58ZJ8iz5XP07MnC33t/f3+eeOIJNq7fwOiVE3m143CWe/7B2bNnAfDz8+PIkSO0b9+eAwcO0L59ezZt2sSff/7JCy+8wJEjRxgzZgwbNmwgLi6ODz/8kFWrVhETE0P//v2vmHHxC4vZ/MVm/Kr4cS7lHNumbmN6t+kk73f8Rbzy9ZVYvayFBTqiTQT3/XYfqYdSmd5tOus+WEedAXWI7BhZtl9mAbmM0EgFFwDleHrR4L6BtGvn+JPro48+YuvWrVe9EeGee+4hIyODvLw8HnzwQerUcfxSjR49mu7du5OXl1fqUzgJUVL5ublYrK6EVL8dgBCbnfua9aT/t8PJV/l8+fO0wt/7fz/zHO12Veb5BWN4a83neB4P4KeffgLgrbfeYsyYMbi5uVG3bl2eeOIJvLy8GDRoEACvvfYa1atXL5w4OC8vr9gZs1OycfV2pf+M/li9rJw9epax4WOJnxpPmxfasG/+Pmr3r41XpQs3EVXrXI0R+0aQtCMJn3AfKkRVKKVv7OrkMkKDjKzyAedcTET9sz4LT6fRwNuDPpX84aIus7/1n5XTdX9bX57WlQKtNdp+4XFhxaXbXO/yorYtVNR/j7JeXlJ/2cWOpbEc3b2Tqt4NqGC5MHa9UmAPNuFTLbhwWc6+VPJOZgHg06caZlcLde+tizKVbZ9y4vpEvmrxFXUG1KHfd/34Y/Yf/PiPH+nxaQ+0TTN/+HwGrRxE5bY39q9auZGnvAnxInDdcVL+u4rzIyTMNzSQEDdCBNtJAS53Yn375d/y604AtF1T/8H6ZZYMILx5OJ3e7cTi/1uMq68re+ftJaRxCOHNw/mm8zdUbleZiDblZ3J2aYEbJDfXRtKZTHRBUyvAYils9fztv0kxWnRlsa7YOcppxtKgtUaZ1IWHUmXb4i1i29Jo4ZdkeUldrq5ou+Zc+llO7N5DxcAI3H18Lj1WwXvOv1dZzFj8XJlQfwLa5lj2qi7dcbuLyv5d7+/YO9dx8j+gRgCZSZm4eLgwaMUgKtS4cV0k50kLvJyxWs2Eh/hcfUMhbiqBRDSrdk3v+PfJfzOmouOmtvycfCyuZVu2Di45yP5F+wluFEy1LtXYPXs3le+oTPdx3fGr4lemx75WUsCFEOWaRwWPwudvur3J4DWDCW8ZXmbXWB9acQh7np3u47oT0SaCTu+U3+GY5TJCIUS51/qF1oXPv279Ne+HvE92WnaZHKvJ0CZ4BXsx8/6ZnEs5VybHKC1SwIUQ5V6ntzvxiv0Vhm0dBkDmyUzWj1tfJsfyvc2Xe3+5l7MJZ9n8xd9nuC9PpAtFCOEUlFIENwgmuFEwJ7acKNWZ3hPWJrBx/EZQ4BvhS8ZJxzjePreV7/NUUsCFEE5D2zUntpwAKNUTijPvm0na4TR8K/uSfjQdrTX1H6pP3XvqltoxyoIUcCGE87jovOXpP09T8faKpbLbijUrknY4jUr1KtFnSh9ua3UbZhdzqey7LEkfuBDCKeSczSF2ZGzha5/w0uve6D+jP+1ea0fC2gSmxEzh4xofk5aQVmr7LyvSAhdClHt2m50J9SeQdjiN0OhQun7YFatnyce63/PbHla+vhKLmwWrt5WIthFEtInAbDWz9D9LSTucxqFlh2gwsPT62cuCFHAhRLmWvC+Zhc8sJO1wGtW6VOPBhQ9e9z5XjFrBsbhjeFT0wDPIs/CuS4AKURVw9XUlvEX4dR+nrEkBF0KUS5mnMpnebTrHtxwHDSFNQujxaY9S2XeHNzsw64FZZKdm02hII+6bcx+ph1PxquRFYO3AUjnGjSBjoQghyp19C/cxvdt0AJo+2ZTmI5qX+hgkWaeziP2/WLZO2orPbT50+7AbtfrWKtVjlBaZ1FgI4RS01ix8eiEAvT7vRY+Pe5TJAFIeFT246+u7eHj1w7j7uzOj3ww2TthY6scpS1LAhRDlRnZaNmvfX8vp3aep90A9mgxtUubHjGgdwdBNQwmoEcDWSVvL/HilSfrAhRCG01rz+5jfWfKfJWibJqheEF0/6Frmx81MymTb9G3s/GEnyXuTafxo4zI/ZmmSAi6EMNwvg34hfmo8kR0jiRkVQ0Trsp80YddPu5j1wCxsuTaCGwXT+b3ONHuy2dXfWI5IARdCGCp+ajzxU+MJjQ7lwQUPYrLcmJ7d1e+sxi/Sj3tm3eNUV55c7KrflFLKTSm1QSkVr5TaqZQaddG64UqpPwuWv1u2UYUQN5vcjFx+/ufPADww/4EbVrzzs/M5GX+SmnfXdNriDcVrgecAHbTWGUopF2C1Umo+4A7cBdTXWucopYLKMqgQ4ubzfZ/vAej6YVc8KnpcZevi+9tE1JpLXh9cdhB7vp2wZmGldkwjXLWAa8eF4hkFL10KHhp4HHhHa51TsF1SWYUUQtx8dv64k4NLDgKwcfxGNozbcEmRLdZD/31Zcef09K/mT7XO1za9W3lTrD5wpZQZ2ARUBz7RWq9XSkUBbZVSbwLZwL+11n+7iFIpNRQYChARUX5mcxZCGMs/0p/IDpG4V3DH7GK+ZPJoTFw6mXTBhNLKfNHzi7dXRWxfxDYms4la/Wph9Sr5eCrlwTXdiamU8gNmA8OB74GlwFNAU+AHoKq+wg7lTkwhhLh2pXInptY6FVgOdAMSgVnaYQNgB0pncF4hhBBXVZyrUAILWt4opdyBTsBu4GegQ8HyKMAKnC6roEIIIS5VnD7wEGBKQT+4CZihtf5NKWUFvlZK7QBygX9eqftECCFE6SrOVSjbgEaXWZ4LXP/AvEIIIUpEBrMSQggnJQVcCCGclBRwIYRwUlLAhRDCSd3QKdWUUqeAw39ZXBHnu/zQGTOD5L6RnDEzOGduZ8wM15a7stb6b6Nu3dACfjlKqbjL3WFUnjljZpDcN5IzZgbnzO2MmaF0cksXihBCOCkp4EII4aTKQwGfaHSAEnDGzCC5byRnzAzOmdsZM0Mp5Da8D1wIIUTJlIcWuBBCiBKQAi6EEE7KkAKulGqglFqrlNqulJqjlPK5aF39gnU7C9a7GZHxcorKrZSqopQ6p5TaWvD4zOisF7vS912wPkIplaGU+rdRGf/qCt91s4u+53il1N1GZ73YFXJ3VkptKli+SSnVweis510hcwWl1LKC343xRuf8q6vUkReVUvsKJl3vamTOv1JKNVRKrSv4HY5TSjUrWG5VSk0q+DzxSqmYq+5Ma33DH8BGoF3B88HA6wXPLcA2oEHB6wqA2YiM15i7CrDD6HzXmvui9TOBH3FMi2d43qt81x6ApeB5CJB0/nV5eFwhdyMgtOB5XeCo0VmLkdkTaAM8Bow3Ouc15K4NxAOuQCSwv5zVkUVA94LnPYDlBc//BUwqeB6EYxpL05X2ZVQXyu3AyoLnsUC/guddgG1a63gArfUZrbXNgHxFKSp3eVdkbqVUH+AAsPPGx7qiy2bWWmdprfMLlrtR7Clsb5iicm/RWh8rWL4TcFNKuRqQ73KKypyptV6NY87b8qio3+u7gO+11jla64PAPqCZAfmKooHzfy34Aud/L2oDS6BwkvhU4Io3+hhVwHcAdxY87w/cVvA8CtBKqYVKqc1Kqf8zJF3RisoNEKmU2qKUWqGUanvjo13RZXMrpTyB54FRBuW6kiK/a6VUc6XUTmA78NhFBb08uNLvyHn9gC1a65wblurKipO5PCoqdxiQcNF2iQXLyoungTFKqQTgPeDFguXxwF1KKYtSKhJowlX+WxRrVvqSUEotBoIvs+olHH/ujFNKvQL8imNGn/N52uCYJDkLWFIwmeeSssr5VyXMfRyI0FqfUUo1AX5WStXRWp+9IaEpce5RwFitdYZS6sYEvUgJM6O1Xg/UUUrVwjFb1Hyt9Q1rJZY0d8F76wCjcfy1ecNcT2YjlTD35X6Zb+hfalfJ3RF4Rms9Uyk1APgKx1SVXwO1gDgcY0b9Dly5cVIO+oOigA0Fz+8FJl+07r/ASKMzXi33ZdYtB6KNzliM73sVcKjgkQokA08anfEav+tlzvBdF7wOB/YArY3Odi3fNTCIctgHXlRuHC3aFy9atxBoaXTGi/KkceEeHAWcLWK734HaV9qXUVehBBX8NAEvA+ev2lgI1FdKeSilLEA7YJcRGS+nqNzKMfGzueB5VaAGjn7lcqGo3FrrtlrrKlrrKsCHwFta63JxtcEVvuvIgt8NlFKVcfSDHjIo5t9cIbcfMBdHYVljWMDLuML/j+XaFXL/CtyrlHIt6IqoAWwwJuVlHcNR28AxMfxegIK651nwvDOQr7W+Yv0zqg/8PqXUHhyz2x8DJgForVOAD3CcXd4KbNZazzUo4+VcNjdwB7BNKRUP/ISjXzbZoIyXU1Tu8qyozG2AeKXUVmA28ITWujwNJVpU7ieB6sB/L7oMMsiokH9R5O+HUuoQjv8nBymlEpVStY2JeFlF1ZGdwAwcjb8FwL90+boY4lHg/YJ68RYwtGB5ELBZKfUHjnNTD11tR3IrvRBCOCm5E1MIIZyUFHAhhHBSUsCFEMJJSQEXQggnJQVcCCGclBRwIYRwUlLAhRDCSf0/2E0Jqn7/otkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.7 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of 22 tracts that were used less than 0.7 of expectation in MO\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        counter +=1\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "print(\"map of {2} tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE, counter) )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.3 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.3\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13378706402097285 = MO std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13588673495610878 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for MO\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  8.2588\n"
     ]
    },
    {
     "data": {
      "image/png": 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f5v8vq3RgY2c0HUl+guWzJli+LNXfz/PaknwCeAfLl/4/B7wf+EfgMPBjwAvAvVU1V184uMS63sHy20QFnAZ+77vv/8+TJL8A/CvwZeA7w/D7WP68Zm6P22XW9W7m/Lgl+WmWvwSxheWTkMNV9UdJfpQ5O2ajCJQkaXzG8BafJGmEDJQkqSUDJUlqyUBJkloyUJKklgyUJKklAyVJaul/AbRB4pyrEVXcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00448 0.58283 HD avg vs true 0.57836\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.06302, should have been 0.06802\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for PA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for MO\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 1.247 MO seats out of 8\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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/hy6WLoxdM5a6Y+ty5493sid1j9ERRSHkOvCrlGXPInJCJIlHE3OWvdHxDV7v9Hqe21utVhngSnicSz+Xh04f4ou1jgm9/c3+PHrTo7ze8XV8zb5GR72uyVgoLuZj8uHnu3/m+bbP5yzbd3LfRdvkPnFpsVho3749gJzMFB4j+3OZ3aioFlyNt7u8zfIHlnNT9Zt4Z9k73PD5DYxdPZZzF84ZnFZcSlrg12jXiV3UHVuXfjf249u+3+ZMZ5V94tJms2E2m4mOjga4bJm0xoUnm/PPHN5d9i5xB+KoV7EeP971I82qNDM61nVHWuBuUrlUZQAahTa6aC7CvE5c5l6WlZVFYmJiPnsVwnhxcbBpVh8+arCShUMXcibzDJETIrnvl/s4m3nW6HgCuZX+mgX7B9OlZhc+ivsIS1kLgxoNorRf6ZwTl9mt7ePH6xEX509q6i6qV98PwLp16zhz5gylS5emadOm0hoXHiMuDrp2hcxM8PODRYu6kfhwIu8ue5fPVn/G3tS9zBk8R+bgNJi0wF1gyp1TuDH0Rh76/SEsn1iYsWUGFouF6OhoOnfuTMOGD3LPPaGMHl2WhQs7k91rpbVmx44dJCQkMHnyZOkXFx4jNtZRvG02x2NsLIQEhfDxrR/zQ/8fiDsQxy3f3nJF90MI15MC7gJVg6uy5sE1LHtgGTXL1WTI7CGknE3JOUEUF+dPRgbY7YqGDTflvC/36QebzcZff/1lQHohLtepk6PlbTY7Hjt1+nfdwIYD+XXQr6Slp3H79Ns5du6YUTGve1LAXUQpRbvwdjSr0owsexYLdi9gzcE1AISGbsZstqGUDaV0rvdcvI/9+/czevRoEhISijO6EJdp0wYWLYJRoxyPbdpcvL5X3V5M6z+NTFsmK/avMCakkD5wV+tQowMxiTEM/XkoAN/f+T1hYT5E37edjcfTMVv2YCcYk3b87ry0iJ89e5Y5c+YAyGz2wlBt2lxeuHO7ufrN+Jh8iDsQx+033F58wUQOKeAudn+z++ldtzf/HP+HZ/58huifo2kc3JgjliMkW5KJBw4SSWtac1KfIkyH4a/8LtvPunXrpIALj+bv40+14GokpSUZHeW6JQXcDUJLhRJaKpS/ov/ijdg3WLhzIUGng7iN21ihVxCv4oknHhRUvBDGo6YRKGUHFOfP+1GqVAZ2uz2nJS5XqAhPdVO1m1hpXYld22WyEwPIjTzFJPuW5YTdp4lZO50yIQdYYlpMqYzKtFo1lcOHq3LqVDABAel0776QqlVTLnp/nz59pEUuPM6Pm39k0KxBvN/1fZ5v93zhbxBXJb8beaSAGyAuDkb/+QSzGUv3w//j72/ew2bLbr0oWrSIx2I5QNOmmzCZ/v33GTZsmLTEhUfRWnPXzLv4fcfvHH/uOMH+wUZHKpHkTkwP0qYN+PgdBeDwlnbYbGYc/xQKsLNxYzNOnKgIXPzLdcUKOdsvPItd28nIysCmbWTYMoyOc90ptIArpQKUUmuUUolKqS1KqTedy5sqpeKUUpuUUr8rpeSWrCtg13bHE33paQgTWVk+7N1bE63VRdeKnzhxotjyCVEU/13wX+bunMuH3T8kJCjE6DjXnaK0wDOALlrrpkAzIEop1Rr4BnhBa90Y+Bl41m0pSyCzjxmA6tUOOq8N1zha4A6nTgWTnFzpovdUqFChGBMKUbj9pxzDQtzb5F6Dk1yfCi3g2uGM86Wv80sD9YGlzuULgf5uSVhCBZd19BU2bLiD3r3nOq9CyW5uK06dKsu33w7nwIGwnFZ427ZtDckqRH7e6/oeZmVm1JJRRke5LhWpD1wpZVZKbQCSgYVa69XAZqCvc5MBQJ5n15RSI5RS8Uqp+JSUlLw2uS7VrFUTAKUVLVuu44EHviMkJDnXFgqbzcTevRForTCZ5HSF8BxxcfDee5C68waGNx/OlwlfFmkqNru2k2XPKoaE14ciVQWttU1r3QwIA1oppRoBw4DHlFIJQDCQmc97J2itI7XWkaGhoS6K7f3KlSsHgEKhFFgsB4iK+hNHK/zflvixYxXZsaMuWmuZS1N4hOyRCl991fHY3v9xsuxZxCbFFvi+j1Z+RPkPyhP0ThD1xtaj7w99mbh+ImnpacWSuyS6omad1joNiAWitNbbtdY9tNYtgR8AmQn1CmSfxFTq3xOVZ88GOdcqQFO9+kHKlj1FRkaAzKUpPMalIxXuja9PldJVmJw4Od95YROPJPK/hf+jVfVW/LfNf2lapSlbUrYw/LfhVBldhf4z+jNr6yz2n9wvY41fgULvxFRKhQIXtNZpSqlAoBvwgVKqktY6WSllAl4BvnRz1hIlp4A7W+AAFy74kV28TSY73br9RXi4FVBERfWSa8CFR8geqTB7rPBunf0I8XmNR+c9yhuxb/BGpzdQuQb5sZ608sCvDxDoE8iUO6dQubRjEhStNWsPrWXapmlM3zyd2dtm57znxpAbeeLmJxjWfBh+5suHmhAORbmVviowWSmVfbHyDK31HKXUk0qpx5zbzAYmuStkSZRdwE25/gg6fz4IpTRam9Aa9u8PJyJiP6A5d07mIxSeIXukwthYRzFv0wYibQ+y8sBK3lr6FsfPH+fFdi9SvUx1liQtYcDMAaRnpTNzwMyc4g2Ovz5bVW9Fq+qt6B88msmxSwmtt4eyVY8xa9ssHpn7CGNWj+HNTm9yxw13yMTKeSi0gGutNwLN81j+GfCZO0JdD7ILeNStUcQuiAUgIiIJs9mGzaYxm+1ERCShNZjNJuk+ER7l0pEKfc2+xNwRQynfUny+9nPGx48nolwEe1P3Uq9iPV6/4Rc2zrqBCp0uH+EwLg5u7e5DZmYX/Py6sGgRPP/g88zdOZenFzzNwJ8GYilj4YV2LzC8+XD8ffyL9Xv1ZHIrvUFGrxzNswufZdvQbWxYs4EdO3YAYLWGkZQUQUREEhbLAWrUqEHXrl2l+0R4jd0ndvPdhu/458Q/NK7UmNbqCe6/5zy+vue4cCGImTMr06aNo3DHxsL+/fD1144+dbPZMQb5iy869mWz25i3cx7vr3ifldaV1K9Yn1UPrrpo/tnrQX630stohAbJboFPmzoNs92cs9xiOYDFcoCQkBD69pWxT4T3qV2hNqO6/Htd+N137+DIkdrYbKGYzXZefvkE9etX4JtvIMt5RaFSdpRyzFI1aZKJKVOgXj147jkzIdxG7yN9qB7+FDP3j+Hw6cPXXQHPjxRwg2QXcLvdjhnzZetr1KghxVt4vYSEBLZt83GO96Ow2RSLF5dj8eJ/twkLs9K48UZ27KhP/fo7iI+PJCWlMlu3wm+/OSY9sZfZB4P/pqolghtCbjDs+/E0UsANkvsqlGyOSwo1ZrOZpk2bGhVNCJdZt24dfn7NAEehNpuz2Lcv4qJtKlQ4zsKFPbDZzCQl1cRu//f/hN2uodU46PYiaEW3rKkXXeFyvZMCbpDsAu5j8gG7o3jfcsst+Pv7ExERIa1vUSKUKVOGpk03kpISSnT0FA4erEpMzH05LXKApKQIbDaz8+qr3OfkNHR4B7q8Cjt7Yp7/JY/MCTfk+/BUcn+2QbILeK+evTCZHB/c1atXS/EWJcott9xCePgBBgyYidlsw2QCrS9uQZ86Vc757N87kAHo/pyjeG/vC9Pm8MV74QXO0Xk9kgJukOwCfv7c+ZxWh81mk9vlRYlkMjk+70lJEdjt2WPf/0vr7GW5ljefCPvawfRf6NDexIgRxRbXa0gBN4hd21EoatasidlsRiklt8uLEicxMRGAgIB0ANLTs6/hLsLly7uiIGwVhK0mIMBNAb2cFHCDZE8Ca7FYiI6OpnPnzkRHR0v3iSiRTCbH1SSHD1dxLnEMGeGQTzGf9zmcskDfB/nzT5gwoRiCehkp4AbJPYu3xWKhffv2UrxFidO0aVOU+ne8nwYNtjknMLE7H234+GTh55d++ZvTy8H6B6DSFvA7zbffFmNwLyFXoRjEZrdhNl1+/bcQJYnFYuGWW27Jmc81MnId1asfID09kICA8xw9WoULF3yYN68Xl53EBAjZAell4EIQ1aoVe3yPJwXcILlb4ABWq5WkpCS5CkWUOP7+F49dUrWqY+ISraFKlWSWLm2f6ySmk+856PQGNJkKK54Fbea554ovs7eQAm6Q3AXcarUSExODzWbDbDZLX7goUYKCgvJcnn3rvM2mMJttzqtTQKPh7juhzp8QPwL+HkXdupcPgiWkgBsmdwFPSkrCZrOhtc65lFAKuCgpChoKWSno3HkpdersyRnEbWbaWk7X+RPmfAHxjwAweXJxpfUuUsANkruAR0REYDabc1rgcimhKElyf77zohSEhx8gPPwAVn2A0+HfUHrbYNg+hDPAV19J6zs/UsANculVKNHR0dIHLkoki8XCfffdR2JiImfOnCE5OZnU1NQ8t12v1uGrfXmsXgRTV6Tx0Udl5AaeAkgBN8ilJzEtFosUblFiXfr5tlqtzJw5k9OnT+css2Nnu95OfeqzKaENL710ghEjZOyTgsh14Aa5tIALcT2xWCx07NjxomUH9AHOqXPU0/Vp1SqRPn0qGpTOe0gL3CBSwMX1rmXLlgAsWbKEo6eP8rf6G5M2UfpIO1rd0V/+Ii0CqSAGkQIuhKOIl6tRjq/4iv3sp4/qQ4dWdejZU4p3UUgL3CBSwIWAo2eO8ua+N8kgg2EMo4a5Bj16yGQmRSUF3CB2pIAL8XHcxxw8c5DZfWZT/lx5uQrrCkkBN4i0wMX1LiMrg4kbJtK3fl/6tuxrdByvJBXEIFLAxfVu1YFVHDt3jOim0UZH8VpSQQySVwG3Wq0sW7YMq9VqUCohik96lmMI2dCgUIOTeC/pQjFIXqMRyoBW4noSWspRuA+ePmhwEu8lLXCDXFrA8xrQSoiSrFGlRpTyLcWSpCVGR/FaUsANcmkBzx7wR+bGFNcLP7MfHSM6Mnv7bI6dO2Z0HK8kBdwgeY2FInNjiuvNm53eJPV8Kp0nd2b5/uXYtd3oSF5FCrhB8jqJKXNjiutNZLVI5g6eS1JaEu0ntafpl03ZmrLV6FheQwq4QeQyQiEcutbqyoGnDxBzRwwpZ1PoFtONg6fkxGZRSAUxiBRwIf5VNqAsQ5sOZeHQhZzOPE3f6X05m3nW6FgeTyqIQWx2G2Yls9ILkVvjyo2Z3n86G45soOWElszfOd/oSB5NCrhBpAUuRN561+vNvMHz0Gh6TetF5IRIoqZEsWDXAi7YLhgdz6MUWkGUUgFKqTVKqUSl1Bal1JvO5c2UUquUUhuUUvFKqVbuj1tySAEXIn+31rmVTY9sYnT30ZzPOs+C3QuImhrFrVNuNTqaRylKBckAumitmwLNgCilVGvg/4A3tdbNgNecr0URSQEXomB+Zj+eueUZtjy6hfSXHbfdL05azNcJX7N472IysjIMTmi8QiuIdjjjfOnr/NLOrzLO5WWBQ25JWEJJARei6Px9/Fk1fBUNQhswYs4IusR04aavbyLOGmd0NEMVqYIopcxKqQ1AMrBQa70aeAr4UCllBUYDL+bz3hHOLpb4lJQU16QuAaSAC3Flbg67mc2PbM7pWjl+/jgdvuvAt+u+JfV8KnHWODYd3WR0zGKltNZF31ipcsDPwEhgBLBEaz1LKTUQGKG17lbQ+yMjI3V8fPw1xC05OkzqgI/Jh7/v+9voKEJ4hbg4iI2FTp2gTRtIS09jwMwB/LXnr4u2G997PA9HPmxIRndRSiVorSMvXX5FTUCtdRoQC0QB9wGznatmAnIS8wpIC1yIoouLg65d4dVXHY9xcVAuoBzzBs/j69u+vmjbIN8gg1IWv6JchRLqbHmjlAoEugHbcfR5d3Ru1gXY6aaMJZIUcCGKLjYWMjPBZnM8xsY6lvuafel3Yz8UCj+zH5sf2ZwzQcS6w+t45e9XWH1gtWG53a0o44FXBSYrpcw4Cv4MrfUcpVQa8JlSygdIx9GlIopICrgQRdepE/j5OYq3n5/jdbYKgRUY2nQoUzZOYcORDYSWCmXKxik8/9fzZNmzeGfZO/St35eJfSdSMaiiUd+CWxRawLXWG4HmeSxfDrR0R6jrgRRwIYquTRtYtOjiPvDcxvUcx/rD67n353tzlvWq24uv+nzF+LXjeXf5uzw671Feaf8KDUIbYDaVjLugr+gk5rWSk5j/ipwQSZXSVZgzeI7RUYQoETJtmSzcvZCdJ3bSrEozOtboiFIKm91G86+asynZcYVKxcCKTO03lVvreM9NQfmdxJQp1QwiLXAhXMvP7Efver0vW242mVnz0Bq2H9vOxqMbeX/5+0RNjaLfjf14vePrNKncxIC0riEVxCBSwIUoPgE+ATSr0ozoptEkjEjg1Q6vsmjPIpp/1dyrB8ySCmIQKeBCGCPQN5C3Or/Fnif3oFD8uOVHoyNdNakgBpECLoSxgnyDsGs7kxMn0/CLhoycN5JzF84ZHeuKSAUxiF3bUUoZHUOI61aATwBf9P6CqDpRbE3Zyri14+j3Yz+SzyYbHa3IpIALIa5bD0c+zPwh89Gva8ZEjWHB7gVUHl2ZxuMb89Kilzx+xEMp4AZRSlGcl3AKIQo28uaRJIxI4N0u71LarzTvLX+PaZumGR2rQHIZoUEUCo0UcCE8SYuqLWhRtQXta7Sn/aT2hASFGB2pQNICN4hJmaQFLoSH8jX5ArDSutLgJAWTAm4QpRR2bTc6hhAiD5HVIhnQYADvr3ifUUtGGR0nX1LADSJdKEJ4ri0pW9h3ch8Ar8e+TnpWusGJ8iZ94AaRk5hCGO/SSSKyLdqziDUH11CrfC0GNRyEv9nfqIgFkgJuEGmBC2Gs7EkisoeoXbTo3yIeWioUgJkDZtKiagsDUxZMulAMIi1wIYyV3yQRAN1rdaeMfxme/ONJjz5XJQXcIFpruRNTCANlTxJhNl8+SUTl0pX55NZPWL5/Od8nfm9UxEJJATeIRstYKEIYKHuSiFGjLu4+yXZf0/toF96OR+Y+wpKkJcaELIRUEIPYtR2FtMCFMFKbNvDii5cXb3CMIz5r4CxqlKtBjyk9mLV1VvEHLIQUcINIF4oQnq9SqUqsHLaSZlWa8dDvD3Ey/aTRkS4iBdwgGi0tcCG8QPnA8nze63NS01OJSYwxOs5FpIAbRFrgQniPJpWb4Gf2Y0/qHqOjXESuAzeItMCF8Hwnzp9g+f7lzP1nLpm2TLrW6mp0pItIATeItMCF8GyrD6ym97TeHD9/HIC2lrb0qN3D4FQXkwJuEGmBC+G5Us+n0u37boQGhfLTwJ9oUbUFZfzLGB3rMlLADSItcCE8V9yBOM5knuHL3l/SKaKT0XHyJScxDSItcCE8V+uw1oSVCWPk/JE8Mf8J5u2cZ3SkPEkBN4i0wIXwXBUCK7D0/qXUqVCHsWvG8vi8x42OlCcp4AZSKKxWK8uWLcNqtRodRwiRS83yNVnz0BpCg0I9dkRC6QM30Llz54iJicFms2E2m4mOjsZisRgdSwiRS6vqrVi2fxmnM04T7B9sdJyLSAvcQGfPnsVms6G1xmazkZSUZHQkIcQlXm7/MsfOHePx+Z7XjSIF3EBmszmnH9xsNhMREWFsICHEZdpY2vBah9eISYyRW+mFQ1ZWFocOH0JrjclkIioqSrpPhPBQr3R4hY41OvLwnIf5OO5jj5mMRQq4QbKystBao7XGbrezbt06OZEphIcym8z8eNePdKnZhWf+fIZJGyYZHQmQAm4YXx/fiy4jPHToEJMnT5YiLoSHqly6Mj/f/TP+Zn+W718OwJfxX9Itpht3/ngnrb5uVeyDXRV6FYpSKgBYCvg7t/9Ja/26UupHoL5zs3JAmta6mZtyljg+vj6UL1ceUv9dln0iU7pShPBMG45sIMOWQfXg6mit+TL+SxKPJuasf3Tuo8wZPAcfU/Fc4FeUFngG0EVr3RRoBkQppVprre/WWjdzFu1ZwGz3xSyZypUrh8n07z+BnMgUwrPdEHID9SrW4+1lb1N/XH3KB5YnolxEzvoFuxfQ5ts27Di2o1jyFFrAtcMZ50tf51dOD75y9AMMBH5wS8ISLrsbRSlFz549pfUthAcL9g8m8eFEvr7tayxlLcQmxVLarzQVAytS1r8sM+6awebkzYyYMyLnPelZ6W7LU6Q+cKWUWSm1AUgGFmqtV+da3R44qrXemc97Ryil4pVS8SkpKdccuCQ5e/Ysdrs95/W5c+cMTCOEKIoAnwAebPEgi6IXMW/wPJLPJnPi/Ale7fAqAxoO4OGWD7N031LumXUPNT+rSeA7gQz7dRh2bS9851eoSB01Wmsb0EwpVQ74WSnVSGu92bn6HgpofWutJwATACIjIz3j2hsPUapUKcwnzDl3Ykr3iRDepWfdnhz870FOZZyiQmAFAEbePJI1h9awYv8Kbqp+E82rNGfShkkMbz6ctuFtXXr8K+pp11qnKaVigShgs1LKB+gHtHRpqutEUKkgoqOjSUpKIiIiQrpPhPBCPiafnOINUKt8LVYMW5Hzeuzqsfy8/Wcqlark+mMXtoFSKhS44CzegUA34APn6m7Adq31AZcnu05YLBYp3EKUYDO3zqRBaAPqVKjj8n0XpQ+8KrBYKbURWIujD3yOc90g5OSlEELkaW/qXpbtX8bgRoPdMnx0oS1wrfVGoHk+6+53dSAhhCgJUs6m8Myfz+Bn9mNo06FuOYYMJyuEEC72wl8v8MEKR0/zI5GPEF423C3HkQJuIE8ZEEcI4Vq/7viVasHV+P7O7+lYo6PbjiNjoRhE5sMUouRqXqU5h04f4oW/XuDxeY9zJvNM4W+6ClLAhRDCxe5qcBcAaw+t5cuEL/luw3duOY4UcCGEcKEv1n5B/xn9L1rWuFJjtxxL+sCFEMKFxq4ZS72K9VgxbAVHzhwBoFGlRm45lrTAhRDChTqEd+Cf4//wZfyXNKrUyG3FG6SACyGES33e+3NuqnYTry5+lXWH17n1WFLAhRDiKpxMP0mmLfOy5Vn2LNLS0wDYmrLVrRmkgAshxBVYtGcRDT5vQLkPyhH+Sfhlkzc89cdT7Dyxkzc7vcmQxkPcmkUKuBBCXIFhvw3j3IVzvNXpLWzaRt/pfTlx/gQA5y+cZ8rGKQxvPpzXOr7mlvFPcpOrUIQQwulM5hmeWfAMy/Yvw1LWQq1ytRjUaBAdIxx3U25L2cb+k/sZEzWGkTePpHPNznSN6Uqn7zrxYfcP+XP3n5y9cJaBDQcWS15pgQshhNMLf73A1+u+JqJcBKnnU4nZGEPU1Ch2n9gNwKiloyjtVzqnQLcLb8ece+aQlJZE1NQoPl71MQMbDqRrza7Fklda4AbSyFgoQniS1PRUgv2DmTFgBqX9SnPw1EHCPgkjJjGGF9q9wPxd8xnQYACVS1fOeU/32t3Z9cQuNidvJqxMGPUq1iu2vFLADaKUYufxnYxbM+7i5bnGSLm0/8xT11263pPWuYLWGru253zlLM/1Czj3wGRXuzy/bbPl9+/h7uVXK6/xfvLab2HbpWel42PyYVCjQZiUezsNnmj1BNM2TWP4b8P5of8PrDqwCoAqpavw7fpvSUtP44FmD1z2vkqlKtGlZhe3ZsuLFHCDWMpYWLhnISPnjzQ6ihBewa7t3NvkXrce4+awm/m/bv/Hc389R1n/sszbOY8WVVtwc9jNdP++Ox1rdKRdeDu3ZrgSqjiHNI2MjNTx8fHFdjxPlmXP4mT6yZzX+bXOClp3aSvN1euKmsNTM7qC1hqTMuV8KaXc2uLNb1tXtPCvZvnVyquu5LXfomzXZHwTbNrmWPe6++uV1prbfriNuTvnAlC3Ql2SzyYT5BvEkvuXULdiXbdnuJRSKkFrHXnpcmmBG8TH5EPFoIpGxxDC4x3931FCPgwBICMrA38ff7ceb9HeRfy5+0+aV2lOj9o9+Hn7z3So0YExPccQUS7Crce+UlLAhRAeLXdDJ+CdAFYMW0GbsDZuu8Z6SdISLtgvMKbnGNqFt+P9bu+75TiuIJcRCiE83gttX8h53nZiW6p+VPWiLkhXGtFyBFVKV2HwrMGknk91yzFcRQq4EMLjvdftPeyv2dnwnw0AHD17lDGrx7jlWJayFn4d9CvWU1a+Xve1W47hKtKFIoTwCkopmlZpSvMqzVl/ZL1LZ3qPs8Yxbu04FIrwsuEcPXMUcFwt5smkgAshvIZd21l/ZD2AS08o3jPrHvad3EeNsjU4ePogWmuGNhnK3Y3udtkx3EEKuBDCa+S+zHLHsR3UD6nvkv3eEHID+07uo3Hlxky+YzK3WG7B1+zrkn27k/SBCyG8wqmMUzy78Nmc12Flwly27xkDZvBGxzeIs8bRaXIn6o6ti/Wk1WX7dxdpgQshPJ7NbqPJ+CbsO7mPyGqRfHrrp5TyK3XV+5vzzxxGLR1FgE8AwX7BtA9vT7vwdviZ/Xjp75fYd3Ifi5MWE9002oXfhetJARdCeLRdJ3bx9IKn2XdyHz1q92DBvQuueZ9vLnmT+EPxhASFUKlUpZy7LgHqVaxHWf+ytA5rfc3HcTcp4EIIj5RyNoWoqVGsP7wejaZl1ZZ80esLl+z7nS7vMGT2ENLS0xjefDi/3/M7+9L2Ubl0ZRqENnDJMYqD9IELITzOgl0LqDS6EusOr+Oxmx7jn8f/IX5EPLUr1HbJ/nvU7sG2x7YxtMlQPljxAZ2+60RqeqpXFW+QAi6E8DBaa55a8BQAX/X5irG9xrplAKmQoBAm3j6R5Q8sp3xgefrP6M/4teNdfhx3kgIuhPAYJ9NP8lHcR2w/tp0hjYcwouUItx+zbXhbEkYkULdCXSZtmOT247mS9IELIQyntebDlR/y0qKXsGkbjSs15uNbP3b7cZPPJjN141R+3PIjO0/s5KEWD7n9mK4kBVwIYbj7f72fmMQYutbsypud3qRteFu3H/OnrT8xZPYQMm2ZNK/SnNHdR/N4q8fdflxXkgIuhDBUTGIMMYkxRFaL5I97/8DHVDxl6f3l71OzXE1m3z3b605eZiu0D1wpFaCUWqOUSlRKbVFKvZlr3Uil1A7n8v9zb1QhRElzJvMM9/1yHwDzh8wvtuKdnpVO4tFE7rzhTq8t3lC0FngG0EVrfUYp5QssV0rNBwKB24EmWusMpVQldwYVQpQ8d0y/A4BPb/2UkKAQl+330omoNRe/Xrx3MVn2LFpVb+WyYxqh0AKuHZPWnXG+9HV+aeAR4H2tdYZzu2R3hRRClDwzt8xk0d5FAIxbO44xa8ZcVGSL8nVpoc4u1kVRu3xtutfu7s5v0e2K9PeKUsoMJAB1gM+11quVUvWA9kqpd4B04H9a67V5vHcEMAIgPDzcZcGFEN6tZvmadKnZhYqBFfE1+140ebQJ08WvnRNKm5U55/lF61B5bp/fNmaTmf439qe0X2mjfwzX5IpmpVdKlQN+BkYC04G/gSeBm4AfgVq6gB3KrPRCCHHl8puV/opu5NFapwGxQBRwAJitHdYAdsB1nVhCCCEKVJSrUEKdLW+UUoFAN2A78AvQxbm8HuAHHHNXUCGEEBcrSh94VWCysx/cBMzQWs9RSvkBE5VSm4FM4L6Cuk+EEEK4VlGuQtkINM9jeSZwrztCCSGEKJwMZiWEEF5KCrgQQngpKeBCCOGlpIALIYSXuqIbea75YEqlAPsuWRyC911+6I2ZQXIXJ2/MDN6Z2xszw5XlrqG1Dr10YbEW8LwopeLzusPIk3ljZpDcxckbM4N35vbGzOCa3NKFIoQQXkoKuBBCeClPKOATjA5wFbwxM0ju4uSNmcE7c3tjZnBBbsP7wIUQQlwdT2iBCyGEuApSwIUQwksZUsCVUk2VUnFKqU1Kqd+VUmVyrWviXLfFuT7AiIx5yS+3UipCKXVeKbXB+fWl0VlzK+jn7VwfrpQ6o5T6n1EZL1XAz7pVrp9zolLqTqOz5lZA7u5KqQTn8gSlVBejs2YrIHNFpdRi52djnNE5L1VIHXlRKbXLOen6rUbmvJRSqplSapXzMxyvlGrlXO6nlJrk/H4SlVKdCt2Z1rrYv4C1QEfn82HAKOdzH2Aj0NT5uiJgNiLjFeaOADYbne9Kc+daPwuYiWNaPMPzFvKzDgJ8nM+rAsnZrz3hq4DczYFqzueNgINGZy1C5lJAO+BhYJzROa8gdwMgEfAHagK7PayO/An0dD7vBcQ6nz8GTHI+r4RjGktTQfsyqgulPrDU+Xwh0N/5vAewUWudCKC1Pq61thmQLz/55fZ0+eZWSt0B7AG2FH+sAuWZWWt9Tmud5VweAEWcwbb45Jd7vdb6kHP5FiBAKeVvQL685Jf5rNZ6OY45bz1Rfp/r24HpWusMrfVeYBfgSdPPayD7r4WyQPbnogGwCHImiU8DCrzRx6gCvhno63w+ALA4n9cDtFJqgVJqnVLqOUPS5S+/3AA1lVLrlVJLlFLtiz9agfLMrZQqBTwPvGlQroLk+7NWSt2slNoCbAIezlXQPUFBn5Fs/YH1WuuMYktVsKJk9kT55a4OWHNtd8C5zFM8BXyolLICo4EXncsTgduVUj5KqZpASwr5tyjSrPRXQyn1F1Alj1Uv4/hzZ4xS6jXgNxwz+mTnaYdjkuRzwCLnZJ6L3JXzUleZ+zAQrrU+rpRqCfyilGqotT5VLKG56txvAp9orc8opYonaC5XmRmt9WqgoVLqRhyzRc3XWhdbK/Fqczvf2xD4AMdfm8XmWjIb6Spz5/VhLta/1ArJ3RV4Wms9Syk1EPgWx1SVE4EbgXgcY0atBApunHhAf1A9YI3z+SDgu1zrXgWeNTpjYbnzWBcLRBqdsQg/72VAkvMrDTgBPG50xiv8WS/2hp+183UY8A/Q1uhsV/KzBu7HA/vA88uNo0X7Yq51C4A2RmfMleck/96Do4BT+Wy3EmhQ0L6MugqlkvPRBLwCZF+1sQBoopQKUkr5AB2BrUZkzEt+uZVj4mez83ktoC6OfmWPkF9urXV7rXWE1joC+BR4V2vtEVcbFPCzrun8bKCUqoGjHzTJoJiXKSB3OWAujsKywrCAeSjg/6NHKyD3b8AgpZS/syuiLrDGmJR5OoSjtoFjYvidAM66V8r5vDuQpbUusP4Z1Qd+j1LqHxyz2x8CJgForVOBj3GcXd4ArNNazzUoY17yzA10ADYqpRKBn3D0y54wKGNe8svtyfLL3A5IVEptAH4GHtVae9JQovnlfhyoA7ya6zLISkaFvES+nw+lVBKO/5P3K6UOKKUaGBMxT/nVkS3ADByNvz+Ax7RnXQzxEPCRs168C4xwLq8ErFNKbcNxbmpoYTuSW+mFEMJLyZ2YQgjhpaSACyGEl5ICLoQQXkoKuBBCeCkp4EII4aWkgAshhJeSAi6EEF7q/wFRlGpTO+73+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the MO map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  769362.375\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ztao6VVXbWbizyo4kvTlcm+RDwMmqOjTpWcboqqp6H/C7wK3dofc+2QC8D7izqq4E/heY6Dn9PgTK2yr1QHde5gHg3qr6yqTnGafusMm3gesmO8lIXQX8Xnee5n7gt5L83WRHGq2qOt59PQk8yMLphT6ZA+YG9uy/zEKwJqYPgfK2SmtcdwHB3cCRqvrspOcZhyRTSd7TPf4Z4LeB5yc61AhV1e1VtaWqpln4b/CfquoPJjzWyCQ5v7uAh+6w1weAXl1VW1X/CbyU5Je6RdcAE71QaWz/R93VMsHbKq2aJPcBVwMbk8wBn6qquyc71UhdBdwEPNOdowH4ZFU9NLmRRm4TsL+76vRdwIGq6t2l2D12KfDgwu9SbAC+VFXfmOxIY/FHwL3dL/vfA/5wksOs+cvMJUn91IdDfJKkHjJQkqQmGShJUpMMlCSpSQZKktQkAyVJapKBkiQ16f8A9Urnfnfm/KkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 1.778 1.709\n",
      "fractional expected GOP seats =  5.705  out of  8 seats for MO\n",
      "simpler expected GOP seats =  5.7419  out of  8 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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